From 398c342a32328411738ca7052bee48124bbcf08c Mon Sep 17 00:00:00 2001
From: Asapanna Rakesh <45640029+INF800@users.noreply.github.com>
Date: Thu, 2 Jun 2022 14:16:40 +0000
Subject: [PATCH 1/5] update reading_a_config_file.ipynb

---
 .../notebook/reading_a_config_file.ipynb      | 978 +++++++++++++-----
 1 file changed, 691 insertions(+), 287 deletions(-)

diff --git a/docs/tutorial/notebook/reading_a_config_file.ipynb b/docs/tutorial/notebook/reading_a_config_file.ipynb
index 7789b0d34..85f21f86b 100644
--- a/docs/tutorial/notebook/reading_a_config_file.ipynb
+++ b/docs/tutorial/notebook/reading_a_config_file.ipynb
@@ -1,290 +1,694 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Controlling training and inference with config files"
-   ]
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HuZ6L7hOOn5k"
+      },
+      "source": [
+        "# Config Files & How to Use Them 🚀🌍💫\n",
+        "\n",
+        "Look at the code snippet below:\n",
+        "\n",
+        "```py\n",
+        "# Read a dataset by specifying the path. We can pass other arguments like cache directory and training split.\n",
+        "\n",
+        "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
+        "                                      cache_dir='./logs/cache',\n",
+        "                                      training_split=['00'],\n",
+        "                                      validation_split=['01'],\n",
+        "                                      test_split=['01'])\n",
+        "```\n",
+        "\n",
+        "The `dataset` object is created by explicitly passing dataset-specific parameters to the constructor of `ml3d.datasets.SemanticKITTI` class. Instead of passing these parameters one after another manually, we may use config files to automate our processes. Each config file in Open3D-ML contains parameters (i.e key-value pairs) for `dataset`, `model` and `pipeline` in general.\n",
+        "\n",
+        "\n",
+        "> 📝 **Note:** We use [YAML format](https://en.wikipedia.org/wiki/YAML) for defining our config files.\n",
+        "\n",
+        "\n",
+        "In this tutorial, we will learn how to:\n",
+        "\n",
+        "- Load a config file into `Config` class object.\n",
+        "- Parse data dictionaries from the loaded `Config` object.\n",
+        "- Access individual dictionaries in the `Config` object.\n",
+        "- Access individual elements within the dictionaries."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUBl1Cr_Ox0X"
+      },
+      "source": [
+        "## ⏬ Necessary Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ovckaoW7L8Il"
+      },
+      "outputs": [],
+      "source": [
+        "from pprint import pprint\n",
+        "from open3d.ml import utils\n",
+        "import open3d.ml.torch as ml3d"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "X5tjQ_IbO2zv"
+      },
+      "source": [
+        "Here, we import two modules from Open3D:\n",
+        "    \n",
+        "   1. `utils`: Open3D-ML utilities. Used for reading config file in this tutorial.\n",
+        "   2. `ml3d`: Open3D-ML PyTorch API library. Used for building multiple datasets, models and pipelines."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l_mUk15iYs1M"
+      },
+      "source": [
+        "## 📖 Loading YAML Config File\n",
+        "\n",
+        "`cfg_file` contains relative or absolute path of our config file. First, let us have a look at the contents of our config file as a raw text document."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ps9uy8PdMP3n",
+        "outputId": "398b5617-ce7c-49d2-a0d7-559f6049bd63"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "dataset:\n",
+            "  name: SemanticKITTI\n",
+            "  dataset_path:  # path/to/your/dataset\n",
+            "  cache_dir: ./logs/cache\n",
+            "  class_weights: [55437630, 320797, 541736, 2578735, 3274484, 552662, 184064,\n",
+            "    78858, 240942562, 17294618, 170599734, 6369672, 230413074, 101130274,\n",
+            "    476491114, 9833174, 129609852, 4506626, 1168181]\n",
+            "  test_result_folder: ./test\n",
+            "  test_split: ['11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21']\n",
+            "  training_split: ['00', '01', '02', '03', '04', '05', '06', '07', '09', '10']\n",
+            "  all_split: ['00', '01', '02', '03', '04', '05', '06', '07', '09',\n",
+            "  '08', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21']\n",
+            "  validation_split: ['08']\n",
+            "  use_cache: true\n",
+            "  sampler:\n",
+            "    name: 'SemSegRandomSampler'\n",
+            "model:\n",
+            "  name: RandLANet\n",
+            "  batcher: DefaultBatcher\n",
+            "  ckpt_path: # path/to/your/checkpoint\n",
+            "  num_neighbors: 16\n",
+            "  num_layers: 4\n",
+            "  num_points: 45056\n",
+            "  num_classes: 19\n",
+            "  ignored_label_inds: [0]\n",
+            "  sub_sampling_ratio: [4, 4, 4, 4]\n",
+            "  in_channels: 3\n",
+            "  dim_features: 8\n",
+            "  dim_output: [16, 64, 128, 256]\n",
+            "  grid_size: 0.06\n",
+            "  augment:\n",
+            "    recenter:\n",
+            "      dim: [0, 1]\n",
+            "pipeline:\n",
+            "  name: SemanticSegmentation\n",
+            "  optimizer:\n",
+            "    lr: 0.001\n",
+            "  batch_size: 4\n",
+            "  main_log_dir: ./logs\n",
+            "  max_epoch: 100\n",
+            "  save_ckpt_freq: 5\n",
+            "  scheduler_gamma: 0.9886\n",
+            "  test_batch_size: 1\n",
+            "  train_sum_dir: train_log\n",
+            "  val_batch_size: 2\n",
+            "  summary:\n",
+            "    record_for: []\n",
+            "    max_pts:\n",
+            "    use_reference: false\n",
+            "    max_outputs: 1\n"
+          ]
+        }
+      ],
+      "source": [
+        "cfg_file = \"../../../ml3d/configs/randlanet_semantickitti.yml\"\n",
+        "!cat {cfg_file}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hKMexYybYkP-"
+      },
+      "source": [
+        "Here, we can see that the file is divided into three different parts - `dataset`, `model` and `pipeline`.\n",
+        "\n",
+        "Let us load the config file as `Config` class object. This can be done with the help of `utils.Config.load_from_file` which takes path of config file as input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5D_L3_83QOKn"
+      },
+      "outputs": [],
+      "source": [
+        "cfg = utils.Config.load_from_file(cfg_file)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MuX4CWK2bDSx"
+      },
+      "source": [
+        "## 🩺 Examining Dataset Dictionaries\n",
+        "\n",
+        "Let us try to access the contents of `cfg` object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CQ0-UwSDnjE9"
+      },
+      "outputs": [],
+      "source": [
+        "pprint(vars(cfg))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zh2lMW9-a9uA",
+        "outputId": "96bf255e-6f32-4176-80a9-19c8bd35c91e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "dict_keys(['dataset', 'model', 'pipeline'])"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cfg.keys()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LFLfMDWZgAiF"
+      },
+      "source": [
+        "`cfg` has three dictionaries - `dataset`, `model` and `pipeline` (like we saw in the raw YAML file).\n",
+        "\n",
+        "Let us explore them.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FxF0zIk_gENP"
+      },
+      "source": [
+        "### 🔎 Accessing Individual Dictionaries"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jcXRnojldAg4"
+      },
+      "source": [
+        "The first one is `cfg.dataset` dictionary:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4RJAlyYXdFNE"
+      },
+      "outputs": [],
+      "source": [
+        "pprint(cfg.dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m6BwrYfefhXB"
+      },
+      "source": [
+        "Similary, let us access `model` and `pipeline` dictionaries:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8kS7MiWXe5WJ",
+        "outputId": "6f2fc220-f915-43eb-fe25-bf0d7e61af16"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'augment': {'recenter': {'dim': [0, 1]}},\n",
+            " 'batcher': 'DefaultBatcher',\n",
+            " 'ckpt_path': None,\n",
+            " 'dim_features': 8,\n",
+            " 'dim_output': [16, 64, 128, 256],\n",
+            " 'grid_size': 0.06,\n",
+            " 'ignored_label_inds': [0],\n",
+            " 'in_channels': 3,\n",
+            " 'name': 'RandLANet',\n",
+            " 'num_classes': 19,\n",
+            " 'num_layers': 4,\n",
+            " 'num_neighbors': 16,\n",
+            " 'num_points': 45056,\n",
+            " 'sub_sampling_ratio': [4, 4, 4, 4]}\n"
+          ]
+        }
+      ],
+      "source": [
+        "pprint(cfg.model)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c5gA4AAbfz6T",
+        "outputId": "e8430591-3914-4e1f-f268-291aff5fa00b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'batch_size': 4,\n",
+            " 'main_log_dir': './logs',\n",
+            " 'max_epoch': 100,\n",
+            " 'name': 'SemanticSegmentation',\n",
+            " 'optimizer': {'lr': 0.001},\n",
+            " 'save_ckpt_freq': 5,\n",
+            " 'scheduler_gamma': 0.9886,\n",
+            " 'summary': {'max_outputs': 1,\n",
+            "             'max_pts': None,\n",
+            "             'record_for': [],\n",
+            "             'use_reference': False},\n",
+            " 'test_batch_size': 1,\n",
+            " 'train_sum_dir': 'train_log',\n",
+            " 'val_batch_size': 2}\n"
+          ]
+        }
+      ],
+      "source": [
+        "pprint(cfg.pipeline)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0nsPOf9HhwEX"
+      },
+      "source": [
+        "### 🔭 Accessing Individual Elements within The Dictionaries\n",
+        "\n",
+        "> 📝 **Note:** The dictionary items within `Config` class object can be viewed & updated just like a standard Python dictionary. It is mutable.\n",
+        "\n",
+        "List all the keys available inside `dataset` dictionary (just like the built-in `dict` data type) using:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m4uYJwpNf2NI",
+        "outputId": "d22bdd79-987a-4ad3-ca92-2221e24964be"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "dict_keys(['name', 'dataset_path', 'cache_dir', 'class_weights', 'test_result_folder', 'test_split', 'training_split', 'all_split', 'validation_split', 'use_cache', 'sampler'])"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cfg.dataset.keys()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HVSQdzSVkCvj"
+      },
+      "source": [
+        "We may access any of the available keys and even update their values as shown below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "3b5kKjXqi49F",
+        "outputId": "d8c9805f-2c28-47f4-a790-4fcb5d93df08"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'./logs/cache'"
+            ]
+          },
+          "execution_count": 52,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "cfg.dataset['cache_dir'] # Access individual element"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "MzWyZOCzkcYm",
+        "outputId": "dfc5ac13-d665-4362-8327-19a936e54968"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'./logs/new_cache'"
+            ]
+          },
+          "execution_count": 55,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Update individual element\n",
+        "cfg.dataset['cache_dir'] = './logs/new_cache'\n",
+        "cfg.dataset['cache_dir']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_Tg6uATMlS2_"
+      },
+      "source": [
+        "We may do the same for any of the individual elements of `cfg.model` and `cfg.pipeline`. Try it yourselves!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K3nfhHYOlMPT"
+      },
+      "source": [
+        "## 🏗️ Initializing Dataset From a Config File\n",
+        "\n",
+        "We saw how to load, probe and mutate config files in the above examples. \n",
+        "\n",
+        "Let us now explicitly create a `dataset` object which will hold all the information from the `cfg.dataset` dictionary\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "h6F0X4qUktSw"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = ml3d.datasets.SemanticKITTI(cfg.dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kGS_BcrVoCly"
+      },
+      "source": [
+        "Properties exposed by the newly-created `dataset` can be accessed using the `vars` built-in function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ouh81Bl9nJ7n",
+        "outputId": "68a4379c-5e0c-45ab-aaf9-4338e91313b7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'cfg': <open3d._ml3d.utils.config.Config at 0x7f588ac3b390>,\n",
+              " 'label_to_names': {0: 'unlabeled',\n",
+              "  1: 'car',\n",
+              "  2: 'bicycle',\n",
+              "  3: 'motorcycle',\n",
+              "  4: 'truck',\n",
+              "  5: 'other-vehicle',\n",
+              "  6: 'person',\n",
+              "  7: 'bicyclist',\n",
+              "  8: 'motorcyclist',\n",
+              "  9: 'road',\n",
+              "  10: 'parking',\n",
+              "  11: 'sidewalk',\n",
+              "  12: 'other-ground',\n",
+              "  13: 'building',\n",
+              "  14: 'fence',\n",
+              "  15: 'vegetation',\n",
+              "  16: 'trunk',\n",
+              "  17: 'terrain',\n",
+              "  18: 'pole',\n",
+              "  19: 'traffic-sign'},\n",
+              " 'name': 'SemanticKITTI',\n",
+              " 'num_classes': 20,\n",
+              " 'remap_lut': array([ 0, 10, 11, 15, 18, 20, 30, 31, 32, 40, 44, 48, 49, 50, 51, 70, 71,\n",
+              "        72, 80, 81,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
+              "       dtype=int32),\n",
+              " 'remap_lut_val': array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  2,  0,  5,  0,  3,  5,\n",
+              "         0,  4,  0,  5,  0,  0,  0,  0,  0,  0,  0,  0,  0,  6,  7,  8,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  9,  0,  0,  0, 10,  0,  0,  0, 11, 12, 13,\n",
+              "        14,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0, 15, 16, 17,  0,  0,  0,  0,  0,  0,  0, 18, 19,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  7,  6,\n",
+              "         8,  5,  5,  4,  5,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+              "         0,  0], dtype=int32),\n",
+              " 'rng': Generator(PCG64) at 0x7F588A480410}"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "vars(dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qJnfIBMSozmI"
+      },
+      "source": [
+        "We may reference any `dataset` object property using `{object_name}.{property_name}` syntax. For example, `num_classes` property can be accessed using:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nNAPTS1KnM51",
+        "outputId": "d996d144-29b4-4949-acae-3e64619a34e9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "20"
+            ]
+          },
+          "execution_count": 58,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset.num_classes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dFRA2m3XpUK_"
+      },
+      "source": [
+        "Likewise, to extract information from `label_to_names` property (which maps class label IDs to the class label names), we can call:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "G23BdpxopT1o",
+        "outputId": "4c4baae6-3f11-47a0-fe71-1dc5f7586f42"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{0: 'unlabeled',\n",
+              " 1: 'car',\n",
+              " 2: 'bicycle',\n",
+              " 3: 'motorcycle',\n",
+              " 4: 'truck',\n",
+              " 5: 'other-vehicle',\n",
+              " 6: 'person',\n",
+              " 7: 'bicyclist',\n",
+              " 8: 'motorcyclist',\n",
+              " 9: 'road',\n",
+              " 10: 'parking',\n",
+              " 11: 'sidewalk',\n",
+              " 12: 'other-ground',\n",
+              " 13: 'building',\n",
+              " 14: 'fence',\n",
+              " 15: 'vegetation',\n",
+              " 16: 'trunk',\n",
+              " 17: 'terrain',\n",
+              " 18: 'pole',\n",
+              " 19: 'traffic-sign'}"
+            ]
+          },
+          "execution_count": 59,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "dataset.label_to_names"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mgH74x-4p7Yw"
+      },
+      "source": [
+        "Experiment with other `dataset` properties to see how convenient it is to reference them!"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "authorship_tag": "ABX9TyMuXl08rXCzSMbxSTsTLHiQ",
+      "collapsed_sections": [],
+      "include_colab_link": true,
+      "name": "reading_a_config_file.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Look at the following snippet of code which creates a dataset:\n",
-    "\n",
-    "```py\n",
-    "# Read a dataset by specifying the path. We can pass other arguments like cache directory and training split.\n",
-    "\n",
-    "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
-    "                                      cache_dir='./logs/cache',\n",
-    "                                      training_split=['00'],\n",
-    "                                      validation_split=['01'],\n",
-    "                                      test_split=['01'])\n",
-    "```\n",
-    "\n",
-    "In the code above, the `dataset` object is created by explicitly passing dataset-specific parameters into `ml3d.datasets.SemanticKITTI()` method.\n",
-    "\n",
-    "Instead of passing a bunch of parameters to a function call, we can supply `dataset` information from a specific *config* file. Each *config* file contains parameters for a dataset, model and pipeline.\n",
-    "\n",
-    "\n",
-    ">***Config* files pass information into Open3D-ML in YAML format.**\n",
-    "\n",
-    "\n",
-    "In this example, we will:\n",
-    "\n",
-    "- Load a *config* `cfg_file` into a `Config` class object;\n",
-    "- Parse `dataset` dictionaries from the `Config` object;\n",
-    "- Access individual dictionaries in the `Config` object;\n",
-    "- Access individual elements from within dictionaries.\n",
-    "\n",
-    "\n",
-    "## Loading a *config* file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from open3d.ml import utils\n",
-    "import open3d.ml.torch as ml3d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here, we import two modules:\n",
-    "    \n",
-    "    1. `utils` - Open3D-ML utilities\n",
-    "    2. `ml3d` - Open3D-ML PyTorch API library\n",
-    "\n",
-    "Now, we'll create *config* object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg_file = \"../../../ml3d/configs/randlanet_semantickitti.yml\"\n",
-    "cfg = utils.Config.load_from_file(cfg_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `cfg_file` holds the full path to the particular *config* file - `randlanet_semantickitti.yml`.\n",
-    "The `cfg` object is initialized by the Open3D-ML `utils.Config.load_from_file()` method to hold parameters that are read from the `cfg_file`.\n",
-    "\n",
-    "## Examining dataset dictionaries in the `cfg` object\n",
-    "\n",
-    "Let's examine the contents of the `cfg` object:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vars(cfg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`vars(cfg)` returns the three dictionaries: `dataset`, `model`, and `pipeline`.\n",
-    "\n",
-    "Now, let's explore them. The first one is the `cfg.dataset`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg.dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Accessing individual dictionary items\n",
-    "\n",
-    "These `cfg` dictionary items can be viewed as well as updated like in a standard Python dictionary. We can access individual items of the `cfg.dataset` dictionary like so: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg.dataset['name']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## `cfg.model` and `cfg.pipeline` dictionaries\n",
-    "\n",
-    "We'll later revisit the `cfg.dataset`. Next, let's look at the `cfg.model` dictionary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "cfg.model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just as in the case of `cfg.dataset`, we can access `cfg.model` dictionary items by referencing them individually:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg.model['sub_sampling_ratio']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg.pipeline"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Likewise, individual dictionary items in `cfg.pipeline` can be accesed just like those of `cfg.model` and `cfg.dataset`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cfg.pipeline['name']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initializing datasets from *config* files\n",
-    "\n",
-    "Next, we explicitly create the `dataset` object which will hold all information from the `cfg.dataset` dictionary:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset = ml3d.datasets.SemanticKITTI(cfg.dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we'll look at what properties the newly-created `dataset` object exposes with the Python `vars()` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vars(dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can reference any property of the dataset by using *`object.property`* syntax. For example, to find out what value the `num_classes` property holds, we type in:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.num_classes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Likewise, to extract information from a `label_to_names` property which maps labels to the object names, we call:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dataset.label_to_names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Experiment with other `dataset` properties to see how convenient it is to reference them."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

From cabba7e7a2f8a3d4e715d8f98b5d2e2916d556cb Mon Sep 17 00:00:00 2001
From: Asapanna Rakesh <45640029+INF800@users.noreply.github.com>
Date: Thu, 9 Jun 2022 16:01:23 +0000
Subject: [PATCH 2/5] Re-write tutorial notebooks

+ Condense all notebooks into 3 notebook tutorials.
+ Replace SemanticKITTI with Toronto3D in training notebook (small size & fast training).
+ TODO: Pass through gramarly (may find multiple mistakes), please focus on content & structure.
---
 docs/tutorial/notebook/01_config_files.ipynb | 399 ++++++++++++++
 docs/tutorial/notebook/02_datasets.ipynb     | 407 ++++++++++++++
 docs/tutorial/notebook/03_train_model.ipynb  | 547 +++++++++++++++++++
 3 files changed, 1353 insertions(+)
 create mode 100644 docs/tutorial/notebook/01_config_files.ipynb
 create mode 100644 docs/tutorial/notebook/02_datasets.ipynb
 create mode 100644 docs/tutorial/notebook/03_train_model.ipynb

diff --git a/docs/tutorial/notebook/01_config_files.ipynb b/docs/tutorial/notebook/01_config_files.ipynb
new file mode 100644
index 000000000..de5fe2239
--- /dev/null
+++ b/docs/tutorial/notebook/01_config_files.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e9b3f3ad",
+   "metadata": {},
+   "source": [
+    "# Config Files & How to Use Them 🚀🌍💫\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a10a6f0",
+   "metadata": {},
+   "source": [
+    "Look at the code snippet below:\n",
+    "\n",
+    "```py\n",
+    "# Read a dataset by specifying the path, cache directory, training split etc.\n",
+    "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
+    "                                      cache_dir='./logs/cache',\n",
+    "                                      training_split=['00'],\n",
+    "                                      validation_split=['01'],\n",
+    "                                      test_split=['01'])\n",
+    "```\n",
+    "\n",
+    "The `dataset` object is created by explicitly passing dataset-specific parameters to the constructor of `ml3d.datasets.SemanticKITTI` class. Instead of passing these parameters one by one manually we may use config files to automate our processes. Each config file in Open3D-ML contains parameters (i.e key-value pairs) for `dataset`, `model` and `pipeline` in general.\n",
+    "\n",
+    "\n",
+    "> 📝 **Note:** We use [YAML format](https://en.wikipedia.org/wiki/YAML) for defining our config files.\n",
+    "\n",
+    "In this tutorial we are going to learn:\n",
+    "\n",
+    "🎯 Basic structure of a config file. <br/>\n",
+    "🎯 Reading & loading a config file into memory. <br/>\n",
+    "🎯 Accessing and mutating config file parameters.<br/>\n",
+    "🎯 How to build a dataset component using config file<br/>\n",
+    "\n",
+    "\n",
+    "> We expect you to be familiar with these concepts:\n",
+    "> \n",
+    "> ✅ Machine learning fundamanetals.<br/>\n",
+    "> ✅ Supervised learning fundamentals.<br/>\n",
+    "> ✅ Machine learning project lifecycle essentials.<br/>\n",
+    "> \n",
+    "> *Please note that we tried to present these prerequisites as simply as possible as they go out of scope for this notebook. Some of them may be too simple to sound technically correct so beware."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "be8ece76",
+   "metadata": {},
+   "source": [
+    "### 🤔 The Need For Configuration Files And Its Structure\n",
+    "\n",
+    "In short, they make our lives easier, especially when dealing with something as iterative as machile learning projects and experiments. There are two important reasons for this:\n",
+    "\n",
+    "1. With config files, we can run our experiments through command line. We need not worry about internal implementations / optimisations in any way (atleast during training period). By doing this priority shifts from writing code to improving model accuracy, analyzing training curves and improving results in general.\n",
+    "\n",
+    "1. Machine learning projects have three essential components - a dataset, a model and a pipeline. Pipelines defines how a model interacts and learns from a dataset. Open3D-ML config files are designed on top of this idea, we can manipulate these components using flexible config files which let us plug and play different settings to them. Just a single look at config files (along with training logs) tells everything one needs to know about any machine learning expermiment\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0427755b",
+   "metadata": {},
+   "source": [
+    "### 📚📑 Dataset: Experience in It's Rawest Form. \n",
+    "\n",
+    "In all supervised machine learning algorithms, models learn by \"seeing\" hundereds, thousands or even millions of examples. A dataset is fundamentally a collection of these examples. In our case, a dataset is a collections of annotated point clouds.\n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "acb2fb3f",
+   "metadata": {},
+   "source": [
+    "### 🧠🤖 Model: The Actual Learner.\n",
+    "\n",
+    "Models *operate* on high dimensional data and (usually) reduce them to valuable insights; These operations heavily rely on optimized parameters called weights. Model output schema dependS on multiple factors.\n",
+    "\n",
+    "In our case, model operates on high dimensional point cloud data and reduce it into lower dimensional insights such as - what kind of point cloud it is (entire point cloud classification) or what is type of each point in the point cloud (segmenation i.e point-wise classification) or what are the extents of a specific object (regression & classification). For different usecases we different model architectures. For example, RandLA-Net is an architecture used for point cloud segmentation tasks."
+   ]
+  },
+  {
+   "attachments": {
+    "image-2.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c8319586",
+   "metadata": {},
+   "source": [
+    "### 👨‍🔧🚿 Pipelines: The Learning Process\n",
+    "\n",
+    "Pipelines in Open3D-ML define how a model and a dataset interact with each other. It also includes steps for training and inference.\n",
+    "\n",
+    "The below image shows semantic segmenation using RandLA-Net \n",
+    "\n",
+    "![image-2.png](attachment:image-2.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b79af95f",
+   "metadata": {},
+   "source": [
+    "#### An Example\n",
+    "\n",
+    "`randlanet_semantickitti.yml` is a config file used for semantic segmentation pipeline, using RandLA-Net model architecture and Semantic KITTI dataset. Let us have a look at it's contents like raw text file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40d8af8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pycat \"../../../ml3d/configs/randlanet_semantickitti.yml\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a75070d",
+   "metadata": {},
+   "source": [
+    "#### Read and Load Into Memory\n",
+    "\n",
+    "To be able to utilize key-value pairs and parameters present in the config file, we must load the file into memory as `Config` class object. `Config` class object's usage is very much similar to standard Python dictionary `dict`.\n",
+    "\n",
+    "`utils.Config.load_from_file` takes path to config file as input and returns `Config` class object as shown below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c60d9c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from open3d.ml import utils\n",
+    "\n",
+    "cfg = utils.Config.load_from_file(cfg_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00a48be1",
+   "metadata": {},
+   "source": [
+    "#### Probe, Examine & Access Parameters\n",
+    "\n",
+    "Built-in function `vars` grabs all the properties of the object as dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da50bb7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars(cfg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7af1de18",
+   "metadata": {},
+   "source": [
+    "List all the keys in the top most level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e003542b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1aa31b4e",
+   "metadata": {},
+   "source": [
+    "To access any property, use either `cfg.{property_name}` or `cfg['{property_name}']` syntax.\n",
+    "\n",
+    "Access `dataset` parameters using: (same can be done for `model` and `pipeline`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef76e079",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg.dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a12fd14a",
+   "metadata": {},
+   "source": [
+    "One other way to access `dataset` parameters is accessing like a built-in `dict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0312122",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg['dataset']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7116e971",
+   "metadata": {},
+   "source": [
+    "Accessing individual parameters can be done with either `cfg.{property_name}.{property_name}` or `cfg['{property_name}']['{property_name}']` syntax. Inner levels can be accessed using the same idea.\n",
+    "\n",
+    "Let's try to access `dataset -> sampler`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ae8461fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg.dataset.sampler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b58186c",
+   "metadata": {},
+   "source": [
+    "Another approach:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39176453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg['dataset']['sampler']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddbbcc07",
+   "metadata": {},
+   "source": [
+    "#### Mutate Parameters\n",
+    "\n",
+    "Please note that this will only alter the properties of `cfg` variable in memory. The original `yml` file on disk remains unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5706a884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cfg['dataset']['sampler'] = \"NewSampler\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63b16241",
+   "metadata": {},
+   "source": [
+    "As shown above, we may use `cfg.dataset.sampler = \"NewSampler\"` as well!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32338adf",
+   "metadata": {},
+   "source": [
+    "#### Build Dataset Using Config\n",
+    "\n",
+    "VERIFY: Use `**kwargs`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6181b4de",
+   "metadata": {},
+   "source": [
+    "Explicitly create a `dataset` object which will hold all information from the `cfg.dataset` dictionary:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "913f9c77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = ml3d.datasets.SemanticKITTI(cfg.dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35ca0ffb",
+   "metadata": {},
+   "source": [
+    "Look at what properties the newly-created `dataset` object exposes with the Python `vars()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8abcef5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vars(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a41b47a",
+   "metadata": {},
+   "source": [
+    "We may reference any property of the `dataset` using above syntax. For example, to find out what value the `num_classes` property holds, we use:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a05e5090",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset.label_to_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b89d87c8",
+   "metadata": {},
+   "source": [
+    "Similarly, we may bulild model and pipline components using `cfg.model` and `cfg.pipeline` respectively. Have a look at training tutorials for code examples."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorial/notebook/02_datasets.ipynb b/docs/tutorial/notebook/02_datasets.ipynb
new file mode 100644
index 000000000..663a61406
--- /dev/null
+++ b/docs/tutorial/notebook/02_datasets.ipynb
@@ -0,0 +1,407 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e5b18a95",
+   "metadata": {},
+   "source": [
+    "## 📚📑Open3D-ML Datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc08bb58",
+   "metadata": {},
+   "source": [
+    "In the previous tutorial we briefly discussed what a `dataset` is and how to read build it using config file. In this tutorial, we will dive a bit deeper  on how to read Open3D-ML datasets.\n",
+    "\n",
+    "You may use any dataset available in the `ml3d.datasets` namespace. For this example, we will use the `SemanticKITTI` dataset. However, you must understand that the parameters may vary for each dataset.\n",
+    "\n",
+    "> With Open3D-ML, you may create your own dataset classes! We will talk more about it later ✅\n",
+    "\n",
+    "In this tutorial we will learn\n",
+    "\n",
+    "🎯 General structure of point cloud dataset. <br/>\n",
+    "🎯 Structure oF SemanticKITTI dataset and it's dataset-specific class. <br/>\n",
+    "🎯 Accessing dataset splits. <br/>\n",
+    "🎯 Accessing point cloud and it's atributes within splits. <br/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b3561f",
+   "metadata": {},
+   "source": [
+    "## 🌟 Generic Dataset: How Does it Look?"
+   ]
+  },
+  {
+   "attachments": {
+    "image-11.png": {
+     "image/png": 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"
+    },
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32J8gwPpzl9/++9iB7LynescxW5/IbQ1BwNujB6l0+i0Xrr1/6OTn/559YWDfpcMfad5xcjy3aM208c/07YUiSKlMOeO33UdzCv7LL5mcEGtPQ6Ctt8B8r8ZgfHbb3qM5heumT3TC6ONuOy7HTrhWW/Tuu7J//0XIZFpwMGEwqC5ezL1xI+rbby1DVWTHjxe++SZhMqF0Oj0szJx/ICiKkMnmf1ADAggAMKXSKJOR2GyKj4+l/nu9MnZA6XTvMWOUZ8+WfvqprrjYf/781gZHG6qqKtetU2Vm0oKCjFKp8ty5nOefj123jhkXZ2dblpgBAMa6OkzdeBogglB8fOjh4XhDg0EsRul0y3gmAIAjY6s9Qvc/elUUNQSGMb2FNACAF5cybJLfiMkiJqtpnwSCgOBIVq9+/JpyraxW395WeqXyYhLZ5o9NWCwrIIyJYQROtOO8ikRCRkwWfZGWPOpJfwDAiX3VH86/ceFYLYE3KsbhUwaNF9LoJAAAmYL2H+MrENEK79TrNFh7Y+4e5LUGea3BBQ0hAAnicTrwwIQAYRCPc6m0QmMwAgCyqyUyjXZgeFBrs4jDvHlTkuJ0JtPtaolDETdzPK9IZzJNT+5l7m9AEDAiJnxIZIiNhzgvmPNF5UqtbkpSXN/gAAAAiiBP9+mZEOCXW1OXJ6mzLjkuPio5yN8c8JDIUAGLieF4u7osmFTKN08+enrp88Ojw/Qm0w+nLqZ+telYblGTL2jfkIBJvWLM18JCvblzUhMxnDhTUOroU4VcSJmRIT9xgvPII72PHet95EjSiRPRa9cCAGq2bsXq6wEAhMEg++cfwmQKeeutlMuXE9PTEw8dSjx0KOHAAa+kJAAAmc+P3bgx8dChgEWLAACCCRPMBcz/sa16QdokmDQp+PXXza3fGDmycPlyXVlZ82La4mKEQulz5kxienqf06f9Zs0ySiTiHTsIzN5fdUvMiYcOCSZMaHIvSqeHf/JJ4qFD4Z98AgDg9OvXa/duyzPyeeIJ+5+RJ+qefTnWOHyKZck7MgUZPP7BeafJiF85LT35d01xbr3RgLPYHV8Yxsefbs48HOQjos15LeKJecFnDokPb6/c/EW+F4ec8MiDMcgCPxrf50F/O5tH5npTlTKDWmm0v9+oO+H7OmVifycyzye/XV1bo1KHevNO55ewadSU0EBLAZwgDt/O/+bE+ds1tQ0GI4WEMqlOeVIqnR4A0DfE33ILi0ppcsXKZcEU1MkAANG+3g+uh1IpfmzWjYoardFkXTJe5Ov4pFcEAUlBokOLZxbVyb85eT7tctZzaX8fXDQjOfjBqxEj9GZbnambc8EyubLBYLSz3whyM4JQnD2LkMkBixdThUIAAEAQ3uDBvGHD1FeuGGpqGFEPLoZqi4uNcjnF29t5c6oRMln03HO8kSMrf/pJduSI9NAh+fHjIe+8I5wyxXr0MYnFEk6fTmKxAAAojeY7ZYr08OH6rCxMqSR727ukE9Sa7n9cpNBQSksr6GAYcfD3iv2/lQdFMMdOCyBTEJORuHKqrl5lal7YxdhcysRZQT4i+vqP8q6ckcYl88iUe99DBEWcNLEcchLzfPLjeUVFdQovGu1aeXWMUBDlcy9zJQiwMePq8v3HuAz6lKQ4No2GEcR/d4uVWp3rQ+1SwThJhA//x2kT+UzG9/9d/Dsr1zrLIaHtHfDdBT3U43Jwnc4okQAAanfvVpw8abldW1hI4Li5awShUv1mzFBfvVq7e3ft7t0kNpvdt69g/HjeiBHmPKPT0UNCIlevDnn7bfHvv9f8/nvpJ59Q+Hz+6NGWAhRfX5r/g88hxceHwuOZZDKjTAazHMd1/yynNbVVugvHapOHCF54N5phdQGryTgVN+IKKAAAvRbHMQLcz3IInLC+hqVrwHQazItLse7IYTBJLPbD884irhqKQOhMHcyAB0cGU1DS2cJSHpNeqVDNSEng3Z/+KtU0/HLheiifd3DxTMvyOU0mKFlwGXR2e6b8NGGeuJ4vkbY2HKRdwTgoyscbAJBfKyOIe+fSWoNRrNbQyWQGxbmfXgQBgVw2uN+5ZYHhuCUYAIBYrQEABHDZ1h05Dr4FzufxaZrjcL1emp7e5EYy/0GPODs1NenECfX167IjR5TnzilOnVKcOkX194/56SdmrL1jsNqLIhAELVtGCw4uXrlSevgwb+hQ5H4vKYIgTppYDoGHYVxOa3AcYBjhxSFTqPd+F2QSvT0bIziDVoNlHJUo6gyWgQImI37nihIA4COiUWgP3qayAs2dqwpzooNhxLl/JFWlDTEJbKbXgwMDjYGyeZSqkoaSPI35Fhwn2jmSAXD4VIGIln9LVVPuntfEbgRoz/gnByB0cgePvub55JkllcdyCtV6w9CoUMuhFCcIE45zGDTLLGulVneusKzFtryZdC6DfqWsOl8iNd9iNB+Z7ZMSEkBCkd8vZ5XK7k3/KZEpzheVWwrYHwyLRg0X8KSahv/uFrdr/JnFwIhgLoO+50bO1fIqc9N/Xr+dXSXuIfKJFfq0+XD7FUvl2y7drKtvsNyi1OpO3i0GAMQKG40GPZFXfLvm3vCjSqX6pzOXSCgyMibcuoyDbwEAgEmhhHrzAADpt+52OG9uDQEIaUND2+W6KZROpwYGIiRS9A8/9Lt92/q/5HPnrDMYhEzmpKaGffBB76NH+2ZmCp9+2lBdXbtnj7MjpIpEAABcq7Uec0MQBMAfnLxi9fWYVkvmcKwzMxKb3e2HCXeMobra9oKQ3fOMX1ypS/uusF5lMu//sO7DXCoNTRnmM2Hmg8EQXD7FP5hxJl1cU66NTeLUK003zsvGTQ8suKXa+k1h7/78cdMD9Xo87btCjdoEADDocUWdITtT/sWr2QgCWGzy7Nci/QLbsVCNDfI6w94tZZs+yzfP3AYASCp1GrVJFMwYMKbRoITgSNb1DNmeTWVsHlmtMNVW60TBjCET/KwnWDFY5Phk7u3Liu/fueMfwiCREJXCuGxVfGg0CwBweHvlldP3RndKxXoAwNZvCs1TxK1fImEALbEf/78DNR8tumneQMOgx5MGek9d6KzlYrsxAYuRHOz/d1ZurrguiMexnpTEYdASAvz23Lgz9Ptfx8ZF6k2m9Nv5zz2S5F8l/vL4+f/yS/43YXj8/V0CAnmcR8KC9ty4M2LNb8E8rgnHySh66MWZIXyuVNMw748DZXIlsJrGPHrtNhIJBQD8OG38oIiQEdFhj8ZFHb6dn7RqvfnhOEFE+XrfqaltbzAsKuXJ3nHH84rePXjiu/8u8ph0giCYVMrfLzwjZLPyxHWLdx5S6PQAAL3RpNBqzxaWDfp2C4IgPDrt52cmxfr5JAT4zUpJWHf28qi1WwO5HBOOVynVTCrlvUeHWk+wclxhnXzJrnQAAIdO8+N4mV8cI4YnBvpNTuxhXTLS1/u53/9uMBipZJJ5sveEntEjohtN6HXwLQAAIAh4qndc2uWsTeev7bp224/jBQAwYdj256Y6vs8XAhABszNfPQ+DIN5jx9bt3Vu6ahWJzeakptrTR0JiMs0XhojGSSc9LAwhkdTXrukrKmhBQe2NxaRSydLTeSNGUP38zD2EuF6vOH0aAEANDETpD44d+vJyxenTvk89BVCUMJkkO3YYamp8p0wh83gPgmSxyFyuNj+//uZN/siRAIB7F+BI7RgPSvHxIfP59dnZ2oICVkLbO+h5BKq/v3WO2Fz3zHIwE1FVprXszl1R1AAAiEvmWZdhccgLVkTv31p+6WTd3SxVWKzXrFcj/EMY/+6uqi7VGvT40EkiAIB1PQAAjdpUnFsPABCIaJip07oQfES0KfNDM0/UltytN9fvH8oY8bho5JP+3o1H16IkZPKzwYfSKsyrAqYME0x+NrjJYjkIAsZMDQAA/PtXVUVRA4tNDopgksiI5SkU5dRbl68uvddbY/0SkSnoMy+HiUIY5/6RlBdqAAAUKqrX4UYD3uI4J8gGMooOiw7bfiW7wWB8NC5SxGFb7qKTyWumjvPnem3LvLn5/DV/jtdrIwZMSYo7dOtubb3i8O38hYP6WrIcOpn89ZNjmFTynhs5RVI5h07rGxJg7kfAcKJcrrR0MJgVSe9t+mMez8um036Z9fjXJ85vzLhaJJVH+vA/e2xUmVx5Kr+kA8HMTEngMehfnzh/s7Kmtl4DAIgX+cobtEI2y4DhRVKFVPOgU6HBYCyolQEABCymAcMBABQS+tljo3r6Cy2rAg6PDjOvCti5L37fYP+V44ftu5lbWCfLl0hJKBLmzZvaJ/7lof34zEbL5kb68N8YOWDp7n8KamUcOu3FIalvjx7YZLEcB98Cs0ERIQcXzTTvHmou7+vFUum6z+AnN2InJ4uef75606bcefMsk8AxpdL/hRdEzz4L7i8eo7p06d5saoIw1NTgOh1Kp5uzBwtWz57M+HhNdnbWxIlUf3+ETMYbGvznzbNzdRlDdXXFTz+VfPrpvZnbAJgbogiFwilTrIc80wIDZceOVfzwA4nLNU9fpwiFfjNmWGcwZA6HO2CA5tatgmXLqH5+gETCVKqYn3/2SkwEAFSsWSP7919zSWNdHQCg+IMPUCYTAOA9dmzQq6+a76KHhXH695f988+dWbOofn4IjUbo9dxhw8Lef7/jr3gXYPtIjBCt97WuWnoLEMQ7a7pJxufpzGsWAwBeWx3X2pqBD6FVr2YDAFzwKRUs/3LbnCcn9oLb/HZD5lWJFwxM/n7KOHfH4pDO/ZTKT54seOON1OvXO6W2TtTGDg8EUX/zZvVvv6kuXjSvHENis/1mzjQf7DG1uui995QZGfj9tJLq5+fVp4///PmsuLgm860MEkn1li2yw4eNMhkAAKXTRc8+G7R0qT1B4jpd3f79dQcOaAsLzWFQ/fx4I0f6P/88LfBeKm9ezQ8AEPn11xVr1phXBWSnpAQvW9a8rwXX6ao2bBDv2GHe+JMeGhr+6aes+HgAQMnHH1svXWityT5cmEZTs3Vr3f79+ooKAABCJgsmTgxbudK6b8mzyE+eLHzjjZTWP6Xdsy8HgiAI6q6YsbHJ5861ejeCeCUlRbeynROJzY5es8bOhqhCYeiKFaErVnQgSJROF06fLpw+3Z7CFF/fmHXr2qwwaOnSFnOssJUr7dxSlMRiBS5ZErhkiT2FPYXtvhx46QGCIAiCoO4JZjkQZKeHeiUSCIIgTwSvWHkMNpey8md7dyiEIMh+o2Mj6r95191RQBDU+WCWA0EQBEFuQBEIEg4dcncU3Ry8YgV5OrjYKwRBENQymOVAns5lw2VgOgVBEORhbF2xkkn0Nu6FIAiCIAjqymxlOU1W3YWgLgjDXNaXA+dYQRAEeRg4+hjybNYbeEEQBEGQNTguB/JwiMuyHJhOQRAEeRiY5UAQBEEQ1D3BLAeCIAiCoO4JZjkQBEEQBHVPMMuBPBwBpz5BkAX8OkBQIzDLgSAI6jbgGHkIagRmOZCHc90cKwjq+mBfDgQ1ArMcCIKgbqMzk35Cp+vE2iDILWCWA3k4OC4Hgh7ozK8DQqd3Ym0Q5BYwy4EgCOo24AVcCGoEZjkQBEEQBHVPMMuBPBwcfQxBEAS1os0sBx5CoK4NjsuBIKeBBwDI07WZ5cBDCARBEARBHglesYIgCIIgqHsi27gPw2FHDtTlIQi8aAVBEAS1yFaWQyKh8PgBdXFGA+7uECAIgqAuylaWA0FdH4WCwGlWEARBUItsjsuB/TiQR4A9jhAEQVBL4OhjCLILCUUqFCp3RwFBEAS1A8xyIMguGE4E8TjujgKCIAhqB9tZDrwQAEEQBEGQp7Kd5cBBnRAEQR4DXleFHjp4G9Ns4RUryNPBXByC7oHXVaGHDoraPgbALAfydC66rkoQRKUSniVDDxFDdTUgkdwdBQS1wfYxwNXr5RzfW52dKa+t1usaMPsfZTTgFKodCRkBAAAmE04m25u9GQw41Z6am8EJAm3/Mi0GA06ldU5madTjFHNVjh3lMRNBIjs+iOSRAAAgAElEQVS/OwQBdCbJ15+W8Ah/9FP+Tm/OCRAECeR28lmyCcf33czNE9edLSpLDrL9sjR6mw0mTFLfYPOsnWjyBwIQAIC0vkHgxWyzvFpnYNNp5n+XyZUhfK49rQAAFFo9j0G3/FmjqhdxvOx5oEyj9WY9CMxmo61WAgAwYjjl/oG5oFYW5evdgUqqFOqA+6+tUqvjWj0ju9n6WpJRNMSbOyI63I7w3Inq709g7fihhiC3sH0Ac12Ws+nz/MwTtSiK+IcwQqJYPAGVsPv4LJPovYU0OwsrZUauN8XOwjXlWlEww87C1kwGnNz+9Ki2Sucb0IFfzBZ0OPIm1Aojm2fvy9VhCEAUUkN1acOf60t2/lT8yCjfF96NdnajXdma05fWn7lUrlDRyCQ2jWbE8RKpwv6HEwRhwDAa2e7vL0GY1040YhjFjrNzDMdJ6L2Pt95kumJ3QyYMJ5MefC8MJoxKtqszoElg7WrUGkEQyP3TD73JlFUl7kAlepPJ8to2eUaO4zFodRptvc6gMRxl02kT4qO3zJrcedV39SkjyowM6aFD2oICk1JJYjJNarXjdRIGA0KltlkM1+lQus2fXxwHqKPvNW4woFbB2BlbI/e/rR0OAEFRpJ1fH1yvR2n2HmQtCAxD7OvtwzQaCp9P8fX16t3b/7nnyAJBe9uyFYbNe12U5ayYfU1RZ3j6xbCxUwNc0yLUZf27u2rf5rIVd9RfpCW7Oxb3GPjtljvVtU/07nFkwohQ7zZ7LKBua9WxcxvOXQ1475td86YNiQzpjCq77jC12r17K77/3iiX0wIDmVFRXn36tPdI3BpdSQk9LMzxYoTRiFAcPeVr0oqhpoYqErWrButMvWMBUPh8Erd9Pyx2voZNYA0NJGaLfcNNNeTm0oKD9aWltfv2Vf/yC6tnz9jNm8kcV4whc0WW8/HiLK0GW3+kvwvagrq+sVMDxk4NWPbk5Y8XZ638OdHd4bha6lcbFVp9yUfLeMzO6dWDPNc7Ywa/M2bwtC1/Pb5hx/V3FoV68xyusov25RR98EHd3r2CCRMiv/rK3bFAbqbJySleseLGsGHR69ZxBwxwdnNOH3186b+64hz1uz8mOLshyLO8+2NCcY760n917g7Epd47eKJYqshb+QpMcSCLv+ZPSwz0e3zjzs6orCv25dT8+mvdvn0Rn38OUxwIAMCKi+v199/cIUPyX3mlUyp08xyr9LSK2CSuMBD+pkONCAPpsUnc9LQKdwfiUr9l3lg4qC+c2Qg1cWjRzMJa2en8Eodr6op9OZXr1/tNn+7z+OPuDgTqQqLXrKEIBHkvvOB4VbY/9E7/va0saRj1ZPuuSkIPiVFPiipLGtwdheucL65Q6QyfPzbK3YFAXY4XnRojFPx87qqD9RAASOo1nRJSZ6n5/XfCZAr94AN3BwJ1OcHLlqkyM53dinN3eJBJDCYjkTrcx8F6oG4pdbiPyUjIJAZ3B+Ii6bfyfFuexQ1BYFBkSFZVjYOVIAARerE6JZ7OIv/3X1avXu6OAuqKvMePByRS3cGDTm3FuX05FcUaEqkrXieGuggSCako7lqnns5TUCvz57DdHQXURSUH+8s0WndH0fl0ZWXslBR3RwF1UVRfX/Xly05twrlZTs41JUF0xevEUBdBEETONaW7o3CRGlU9pVNXXoG6E4IAelM3XIIP02iY0Q/14liQDRQfH0ONo12Ytjn3NzcmgYPavQyx2+XeUD43NONMur3LiBE4yLooL86t71hzf20o/Xhxllpp7NjD7VFVqn3j6St2PqPL/9V9+qJz42mOREZjEh6WbXeYNCqD4urVxjts2Z4joSu/z66S2Fm+WlW//uxljaEjnx+NwThl864pm3d17OF2Wn/2sp3PSKppGP7Db+vPOvcUswmhF8uBRVK6LkKv1xYVuTsKqIvSV1RocnIcqcFQXW17ZUKPSUG6oLws5bdv39HrusPpV2Vxw57NZbjL+90ero6+7vtsNQbjy3+mH8/rDgczI4avPpZxpazK3YF0F2QyMy7O3UHYBddq7774YvakSUap1M6H6EpKxNu3d6y5hry8a4MHi9PSOvZwexil0uxJk0o+/tiewrri4hujRyszMpwXT3OMyEhGZKQjNbS5D4nHnFm6QI8k7m9nBrk7CjeoLG7Ysiq/plwbEQ9HjTiRicCB51zA/X7KuO+njHN3FK6mM5m+PJaxIeOK65uuUKhIDm8vALmSUSrNf/lldr9+7g6kE2D19WVff22ornZ3IJ0PZjkPNZMRv3CsbvfGEg6fGhrdtaZm2AtBPCV1IKOop4T6cCqolb381+ELxeWPhAWdLyp3fQDw0+FeKIMRs369u6NwA0ytLvnsM8WpU+4OxCm6T5bz14bSnOvKOcsijuyqvHZWRqWhKcMFk+cEC0QPdiDDMOLmBfnhPyqK8+pRFOk9gP/YnKCQaC/z5fDcG8pVr96atzxq6EQ/gw7/5csCgR9t+GN+uzeVXjsrAwAkD/Ge+kKoebvNvzaUpv9RAQBY9eotgYj2xlc9A0Jb2DtTr8NOHRD/t7/GvLnmyCdEwyeLWtyWnCBAfrYq/Y+KO1eVOE7EJHImzgrq2ZeHoAAAUFWq/eat24/PDR460c/ykDPp4v1by62bLsvXHNhWfvOCnM2jTHkhJCSqjcSl4LZ6y6r85MHe018K37elTFKla9dr7jgSikjFehc32u1pDMZnt+0FACwZ0u+TI6evV1SzqNRZqYlvjx7oazXNWN6g/ebkhT8uZ9fWazh02qzUxNdHDvC/v4X4sj1H9t3MPbR4ZkKAMLtKMunn7ZtmPqYzmj44dLKwTs6h0xYO6vvmqIFeNKr5XqmmAQDgt+KrBQOTW+sEqlbVf3vywh+Xs1Q6faQP//WRA2emJLQ4ItuI4fuzclcdO5dfK0URZFBEyAfjhvULDTR/VY/nFT2xceffC58ZHRtheciyPUfOFJQeWTJbyGaZa9h+Jfvbk+cL6+RJQaJvnhzb5ou2fP+xa+XV300ZF8TjPFW0qz0veScI4nFwAndxo9DDjiDU16+XfvKJrqzMKzGxPivLxe2b5HJn5/a2shy9ztGvnEphdOXpibhc++PK3KBw5uKVMVKJPj2tIu+GaukXcf4hDAAAhhEHtpYfSqvoN8Jn/IxAowH/d3f1l6/dfvb1yH4jfVoc95d3Q3njvKz3AP6rn8eV5NUf31P1/YqcpZ/HCQPpA8b6cviUHT8Wz3g5PDSaxfdpYddZhdSw/qO7RXfUY6b6x/bm5lxT7FpfIqnUPfNSGJnS6JedIMC5fyS/fV0Q14e74N1oMhk5ky7+bvmdqQtDx04LsHM2fu4N5dr3cgUi2vx3osgU9N+/qgR+NNs/m1QqOv+d6AFjfHA3DS7CcULg1+6NcD0UhuGEC8fmXCqtulC8d2hU6NY5T2YUlW8+f/VCcfnu+U+LOF4AgEqletqWP0ukitmpiUOiQi+XVm4+f+14XtEfc5+KF/m2WOHXJ84X18lfGfZIiDd359Vb3/13oViq2DBjUgif8+PTE1b9exYA8OaogeECfosPv1kpnrp5l8ZgXDAwOTnY/4/LWa/uPlwmV7z/6LAmJY0Y/t7BExsyrozpEfnW6IENBtPa05mPbdj+1RNjn+3X254huiYcX5n+39rTmePjoz6aOCJPLH0u7e+EAD/bj0oKEq1+fEyUr3f3GGD0sGnIy8udPz9gwQKURqvcuNEokdBDQvwXLPB5/PEH24IShPLChcp16zTZ2YAgGDExgUuW8IcPN29Fjmu1Ba+/ri8v77F1K0UgUGZkFC5fHrt+fX12dtWmTUaJhCIUBrzwgu+UKSiNpszIyFu4EACgLS6W7NoVumKF3+zZLYRFEPU3b1asXau+cgUAwE5JCV62jJXQ8pZH+srKyp9+kp88ianVFKFQOG2aaO5cEuvemUnJxx+rL10yx2a+xSiV5s6dy+7XL2zlyns1lJeXf/ed4vRpAIBozhzfqVNtv2hGmaxk5UqjQhH9ww+64mLXZzlkPt/ZE7FtZTk0BsnBHIXDoziyg3x7adSmpEHec16LoDNIAIDYRM537+ScPlgzbXEYiYTcuaI4lFYxaXbQ5LnB5rwh4RH+li/yD2wrD+/h1eIeFMV59c+9GTV4vBBBQEI/njCAvv6jvNJ8jTCQHhTOrFcaAQChMaweSS3s/koQ4Gy6pPRu/aufxyX04wEAEvvzfUT03ZtK+4/2jerVaARMRZFmz+bS/qN9LcH3HsDfua7k0O8VMQmcyJ5tD5fRabFju6sDwpgvfRTL86ECAHr14/36ZYFMYqunJCKebR6LY8DccxL5UPXQk0guvWIlb9AuGZL62WOjKCT0icQeA8ODnkv7e/uV7NdHDjDh+OdHzxTUyrbOeXJ8fBQA4LFeMePjo6Zu+Wv1sYwNMybRW9osurhOvnvB9N6BfgCAcfFR89P2Z5ZUVCpUkT7eI2PCf7t4HQAwNi6KRW1hV2edyfTlsXMGDD/84qykIBEAYGLPmFf+Orwt8+b05F6BvEbz7P65k78h48qiQSnm4AEAjyfGzvh1z6pjZweGB0ULBW0+96xK8bZLNxcO6rv68THmGh6Ni7S9URSLSvlgXNN8y5UqlCp4xcpxNdu24Tqd79SprF69xNu3F69cqS0sDH7jDYREAgQh3r69bPVqZnx82IcfolSqeOfOgmXL/F94IfDFF1vcIJ0wGEo++QRvaAh66SWUThfv3Fn62WdGiSRo6VJmbGzEF1+Uf/cdMybGb8YMenh4C9Hcb5EeEhL2wQcAgKrNm3NfeCHmxx+bryHUkJOTt2QJwHHRs88yY2OV585VbdigysyM+vprim/LJx5Na7h7N2/RIsJoDHzpJVpQkHj79vqbN3G9rUMAgiD80aOFM2dShUJdieMbjHRF3WqwG4dPGTstwJwlAACCIlkpwwRZFxUyid5kJK6ckQaGM4c/JrJ0jbDY5GGPiSqLGwpuqVusMDCc2SuVZ8nTgiKY3kKaVmOyJ5gGtSkrU5400Ds28d4vOIKA+BSeMJBeXth0Hbwb5+U4RlgHT6agQyb4kcjIzQtye5oTV+jys1WpIwS8+71KdAZp4FihPY+FXKNWramtd92OFuEC3svD+lmuB42KjegfFrw/K1feoC2VKU7kFT+R2GNMjwdXfFJDAyf2jD5bWFpc1/JHbkRMeE//e7+2dDJ5QHiw1mhqMNj1dSiuk18oqZiSFNc78N5+LxQSOiUpHiOIXHGjHVv1Juyv63dC+Fzr4L2ZjBeHpJTLVacLSu1p7lhuEY7jM6wuh/UKED4a59BUDqcjAIp2y6nkhLG21mWtmZTKiC++CH79de+xY3ts3uzzxBO1e/c25OYCADR37lSsXcsdMqTH5s2+Tz0lmDSpx5YtgkmTan77rf769RZrwzQaAsNit2zxnTpVMGlSzJo1rPh45YULJpWK4uPDHTSIzGLRAgN5w4fTQ0ObP1xXUlK1eTNnwID4nTt9p071nTq1x5YtZB6vJi2tSfKB1deXffUVACD2558DlyzhjxoVtnJlxKpV9devi//4w56zIwLDxGlphNEYu3Gj/7x55qdPj4zUV9maLUj29g5atowq7M5HCptZjsNnFlKx3pWroPn4072FD64ckUhISBRLXKmV1xr0WqyqROsfzGCyG02s9w2gC0S01nZTYvMoNMaDJ4CgCIICpcyuJT2UcqOsVi/wo1HpD2oICGV8vCVpxOONNvYyGvCacq3Aj9bkspe3kOrjT5dU6Qx2XDqUVOpUcmNQeKMNBMzPzp5o3aU7/qi3ypfNcuUODzFCgYD1oDkOnRYn8ilXqGpU9eVyVbVKnRjgR7aa1ENG0T5BIolaU6lsOekXcbysy5NQRKM3qHR2jeWqVKolak20r7d13+6YHhFF/1s6OSHWuqRap88T10X4eAuYjQa6Rfp4C1jMW9Vtr3ajN2G3qiV+HK9gqy4i87OzJ1R3CeJxuucaqghiZ1dEp/B+9FHu4MH3WiaThdOnAxyXHT0KAJAdPQpwPGDRIpLXvcFnKI0mfOYZhESSHTvWaoVjxlD97l3rJPP5zPh4XKMhTHYl96rMTJNUKpozx9IiLSCAN3iwoabGWNcouW/Iza2/ccNv+vQHs+4RhDd4sFefPvJTp0zyts91DWKx6tIl3tChzNhYy9P3mTzZcsGra9KXl+srnLtns80cpDMuNtmcx97JfEW05gN7MROB4wROEBhGICiCtOfAyuZSyM1WNdQ1dPJTInCAmQhzCtVh5h4m1AP30yjKafmY2v0YTJjBhYvbBnDZTErTi0cYTphwwoTjGE6Q2tlzYBmYbKEzmbRGu37u7YcThAnHySjS4d8fE443GAwogqAetcpehUKFeFTAXRMrPt56jThaYCBVKNQWFpoUCm1hIcXXl+bvb13eXEBfXo5rW95eg2pdHkEQEgnTarF6uxaDbbh7l8ThNEryECT0/fd77txJCwy0LqktKMD1+iYLC5HYbEZkpLGuzmBHZ5ihqsookTB79Gj+9O0J1V1owcG0oCCnNuHcvhyBH82VZ+u1NXqDvlG3B4EDOpNEpaIogpBICIETrhz+aScEBSQyQuBEeydY6LQPDpkMFhkAgGNd7tnZRgAQEefQIj16LabTesbMFCqJRCXbWqOzc1Up1Q3GB/2OBAEwHGdQyEwqmYyiJBTBXL8KpB1QBCGjqAkn2vX7Y8LxhvvrJpNRlEml4gSBe1TXSLftyyEIV67CQgsObuFWHDf/hyAIaOeiRBSfprtN43o9bl8Xpv3MnUMtjg2yE67X43o9ApdcasbpfTmupJQZ1IoHZ5YYRpQXaYSBdL4vjcYgBYQxygsbVPJG15tqq3TSGn1gWOdfR2BzyXwBVSrWW19vUsmNn7+cvWdTKWZ68HNGoaKiYEZ1mbauutE3RyYx1FXrhAF0yzUv67QGwwhJ5YPywkA6h08pvNPoDEMlNyjquvmO3zQGic7wkC+2a79PpTJlvf7Bu6/W6+9KZKHePF8vVjCf489hZ5ZWWm+cZMLx6xU1QjYrkNv5i0P6sb0ELGZ+rcz6OJ5VKY7/9KcfTl20Lsmm02L9fPLEdTXqRh/mwjqZVNPQy//Bian1s2swGEtl9zZEo5FJvfyFFXJVYZ3MUoAgQH7tgz+7oAqFqjM+Il0uT0IQhNq4+8Sp9OWNFzrCcYIgUAYDIZMBihIEAfCueFJkzm/svBBmgev1+P2HoDQaSqMRXfLZ2eL8zN65fTkuJq3RZ56oxe73Z5Tla66cliYN8OZ4U8gUJGWoQFypPfePxFJAozadPlgjCmY0mfHULq11n3hxKb0HeGdnygtuqyw3FufWF95RB0WwSORGP2dJA/l0JunE3zWWPMZkxM8eFhv0eGJ/PgCAwSSx2OSKwgaT8d6HuKZMe+uSwlKDfzAjvi8v44iksvjeGCOdFjt7WGKdTnVBHpZHO0ai1kjUrtuAPb9Wuu9mjvlLTBDg35zCiyXlTyTGcui0UG/eqNjwozkFJ+8WW8pfLq1Mv50/ICwo3KflqeBtstEBEy30HhwRfDA7L6vq3q5qBAGO5hZK1Jo+QY2vIJBJ0/rEV6vUG85dNd6f+idr0K4/e8Wf4zU0KhQAwGXQ2TTqlbIqS2sZReXXKx50GEzoGc2iUX84lanW3RvjmVUlPpid17Hn5RpBPE4XzFE8jrakxPpDqK+uNtbWMmNiSBwOIzLSUF2tK200gF1fWWmQSGjBwSijhQXPHMSIiMBUqiaDr2u2bbs5bpzm1q1GJaOiUBpNffWq9Y2YWq0tLKT4+FDvX/PCTSbrYcvGujqT7F7uTg0IoAiF9TduEAaDdQGjQgG6MENtrbMHp9vsH3N4VVmpWO/KSQMkMnL0zyqpWJ863KesQHN8T5UohDnyyXuTquJTeJNmBx1Kq5BU6lKGCczr5ZQXaJ57K9K80F97MVhkDp/y3wExACC8B5vBanQxAkHAkInCW1cUGz65O3pKQFisV841xbHd1f1H+/Ye2PQoEhTBmrIg9LevC5RSw5CJfub1cm5dVkx+Njg8zgsAwPGmJA3wPrCt3GTEHxnlW1Ou/W9/jSiEYZmuRaWjj80JWvt+7pp3c8bNCOTwKGfSxTnXlU3Sqa6GIMDDsyogh9bCokrOQ0LQ9w+ezKmpHRET/t/d4m2Xbj4aFzU7NREAQEbRdx8der2iZv4f+63Xy+EyaO+MHdLiNHLbaGSSgMX8Oyv3l4vXh0SGJjUb50snk98eM3jq5l1Pbdq5dHh/84o7/9zJXzQoZUB4sKHx8L3x8dGLBqVsyLhSJJVP6xNvXi+nWCr/8vEx0b4CAEC8yHdgRPDa05nyBu2YHpHni8r33LjTU+SrvJ/T9PIXrhg7ePn+Y09u3vXCwGSxSvPDqYtKrZ7l2rcAcj35iRPCKVPMA1xwvV6ycydCpfJGjAAAeD/6qOTPP6t//ZXZo4d5OLC5AGEy8UeP7nCLBIYBgmjx0gd30CCyQCDescMrKcncokmlkp88SWazaQEB1qNtmD16eCUl1R08KBg//t7oHIJQnDtXf/26aO5cMp8PAKAKhUaJRF9RQQsIAAAQJpPs8GFMc/8Q4OfHGzq0ds8e5YULvGHDzAXq9u2zZ+SyG1GFQneul9Mp43Jceek/NMbLvPbxjx/kMr1Iwx4TjZ0W4MW59xxJJGTy3OCAUOax3VXr/pdHZ5B6pvDmvh5hWfu4vYIimJNmB+3/rfzaWeny73vFJDbdW5snoL7+ZdzxPdWnDoj3bCoVBTOmLQptce1jBAGDxwsFftTD2ys3f54PAIjtzXltdbxl7WMSCZk0J8iLSz6yq+r8v7VhsV6zlkZQqMimz/MtlQSGM9/6puf+reXb1xQDAJKHeL/0UY9t3xV25Lm5DAIcXBXQZMQ95QSYTqW4MtQRMWHmtY9/y7wh9GK9PXrw0uGPMO8vZhPIZe9f+MyXx8//df3OurOXfb1YTdY+bhcyii4b0f9OTe2KAycm9Iz+bfYTzGar5vQO9Dvz2rxPj5xedeycee3jNVMnmNc+bpLlUEjoZ4+N6ukvXHs6c+GOgzQyuX9Y0PrpkyxrH7OolJ+fmbTqWMbvl26mXc7qE+S//bkp18qrN52/Zq4BQcCiQSmh3rz/HT41/48DHDpt0aC+TBr1x9OXOvDsIA+CKZV5ixf7P/88xcdHvHOnJisrZPlyZkwMAIAVHx/0yitlq1fnLlggfPpp83o5mqwsv9mz2cnJHWgLpVLJAoHizJnaffvYffs2n0xODwsLWLCgbPXqvMWL/Z55Btfpan77TV9dHfXtt2Rvb+ssh+TlFfLWW3lLluQtXiycPt28Xk7t3r3M+Hi/2bPNKRRvxIiatLSC11/3nzePKhRKDx/WFhVZxiEhJJL//Pnqq1cLly8XPv00q1cv6cGDyowM2/t1u50rxqIRrfvi1ewvXsm2UaBN185K548670gN9vvz55KPFt1UKQyuaQ7qFAtGnb92VupIDV+8mv3FK1mdFY8N3m+vPpR915Eaxq1LG/dTWmfFY0O93vDUpp1PbdpZr4dfB49xKPuu99urHKzE++1VDn5KrclOnLiUlORgJZeSkmQnTnRKPLZpcnOvDhpU/euv4u3brw0ffikhIXvKFPmpUwSOPyiE4/JTp25Nn34pMfFSYuKt6dNlJ04QGGa+E2toyFu8OGviRENdHUEQinPnMuPjFefOWbdS/NFHVwcN0uTmmv9UXrhwY/TozPj4ks8+azksHFddvXp71ixziznz5tVnZVkHXPP775ayurKy/Ndfv/LII5nx8TfGjq346SdTfb11ZfVZWXfmzr2UkHA5Obnovfd05eV5ixcXf/SRpYBBKi359NPLycmXEhJuTZ8uO3Ysa+JE6wI21Pz+e/Pn62x35s69M3euIzW0+Slts2vaQ06TIQiCoK7HxZfMETJZOGOGcMaMVu5GeMOGmS/oNNdkt07uoEH9bt9uUiZs5UrLdgoAAE7//r1bX2vH3CI7OTk+La35PczY2ORz56xvoQUHR33zjY3KWAkJcb/9Zn1Lk+1FKd7eoe+9F/ree5Zb7L8Y5zd7dsubVHg4W1mOQmpwMMmprdaBLjlbFeoiCJyorX5YxuXk1NS2a7km6KGSVVVjdNNGK60xVFfDmcmQp7OV5fC8W9iPpl1CY1geN68NciUcB6ExrlsO2L2SAkWW4bEQ1ESMr4BO6fhyKc5A9fd3fGYyQRCWeUAQ1ASh07lzT3LH51hF9mTjOKFrwOlMp58QTFvUwjYiUFema8BxnLBnL9LuIYjHyc93xX54LCplz4LpLmgI6kS3amrZ3XEKGEqletzSa5DLECYTVeTcrVecm3yQySiDRTq+19ZuYdBD6/jeKgaL1HwPje5qcGRwteph2c4Caq+MorIIH293R9H5yDxek7VhnMQ8zKVbjizpxgwSCS0szKlNOP0A0yOJe+aQ2NmtQJ7ozCFxjySuu6OwFwGIWo1DO4o/0zcBx4k91+90VkhQd3KtvPqJxNi2y3kaZo8eqitX3B0F1EUZZTL+yJFObcLpax/PXxFdV6P/Z3ul41VB3cnh7ZV1Nfr5K6LdHYi9EID4shwdQjQ0KvTDw6c6JR6oO3lt71Eyir40tJ+7A+l8fjNn6oqLcY3rVv2GPEXZV1+RvLw6tliR/Zy+j5UXhzxmmv+fG0pO7HPdhm1QF3diX/VfG0rGTPO3rNn4kDiwaIakXjPj193uDgTqQrZfyd6ccXXF2CGdUpsRc92+9/bg9O9PDw3NnT/f3YFAXY5kxw7fqVOd3YorhkTMeCl8xBOi378r+t8LN6+fg4PtH2rXz8n+98LN378rGvGEaMZL4e4Op106ZyrAzuen/ptb1O/rTTcr4ZVcCDyf9vfCHQfn9Ou9dPgjnVIhpeutdRu7caMmN/fuSy+5OxCoqzCpVNdHjKAFB4e8+aaDVRmqq22v7+yiM0Z9b1AAACAASURBVOlnX4scNlG09dvCHz/IJQhAZ5JodBRpsseV+SCCAEBY/d8edhx9TBhOJqM4TqAOb6xlNOAUSuvZYZP47//fZMJbHGaLYQSJhDR9vk3+3TwGI06hoG28VkTT8kYDTqE2e1RLjzXqcYp5G4o2S5rrbAuBE3odrmvAEASExnp9tDkpNIbV5qO6mM6ZJzIyJrzwo1cfW79j0LdbfFjMOJFPpI93sUyeGGCZaNDqB7pSoQ7ksRu/JW27XFqZGhrY3kc1GExMKsVS+P6bb++LUFQni/DxbtYWIlHXC9l2bSJRIlWECXiWmBUNeh6TZn/8FjUqjajlbStarUetNzSf7lSvM3rRrRfXeBCJTKP1ZrVjr0cjhpfJlTk1taUypQ+LuW76xGf79bb/4R6HFhjYY+PGu6+8cn3oUL+ZMwMWL3Z3RJDb6CsrK777TnbsGCMqqteePY5XSPX3Bzb7L113vSA0hrXy50QAQM41ZVGOWiU3mi+IWX5pzD+fhNW/O5FKYeTwKHotRmM4eqJTU64VBbd791qlzMi1Wn/IkjDUq0wsTrt31GxvDObytdV6X3+7No2yv36pRC8Qtl0nQQAOnxIRx45L9pjhxs7Do9PPvva8pF6z5r/MS2WVZwtL6zTagtq2N9XTm0y09m+lqdbpyxXtntuF4zjqwIpwOqPxRkudVUYTRiHb9R3UGU3XrLYZN2E4mdSReAwmjGpfixYYjpOaPfcWbzQzYjjF7th4DJpab/BmMAaEBX8/ZfyoWM/q0ewgdr9+fTMzC5cvr/7ll4q1a1Emk8RkUvh8k9quTyau16O0dm94h+t0KJ1OGI0IpT1rv7Wy9aalwrbbtSNaG1XZDpgwGBCqfSsO4Di4/4lt1wtoHZvtB9pfLabRIGQyptEQBgPN3z/4tddEzz1nZzwOcsOoiLhkLjzUQZDQi/XpY86dXABBXUrk6tUAAF1pqfLCBUNVFWEy2flAfVWVeSPudtGVldFDQoxyOYXPb8fDrJKDphWWlNDtmPZsTzF9ebllo80mjDIZxbvVNQWMtbUUX982YwCNkxVDdTXV39+eR4HG8dt+LoaqKqp974vm9m1OSgojMtJ77FjQ/lM1RzxcYz8hqMNQBKlQqNwdBQR5PHpoaPPtuyHISR6WBdkgyEE4QQTxOO6OAoIgCGoHmOVAEARBENQ9wSwHgiAIgiBPZXvWJcxyIAiCIAjqnmCWA0F26pxVASEIgiCXcc8cq+pyrbhM2xnbZLWbVKwX+D2Y36/VYAyWK5YKbdJum7c7u12XBWB2N1vVK5UfFsNidfqWDjD3gCAIglrh0izn6J9V/+2vkVTpcIwgkRH/EEZNhc6VAQAASCjAcKu/7V9h2TEoCnC8xdsRAm/hQN1Zx24UAS1V33ZgnQvHiKN/VuEYQaWj4bFe018Mi4hnd1Ldnprm3BVLvz5x/lJZZa1aozdhhGufCIoguMvPM0goijX+tLng+9e8UbdDAEIjk3zZrH4hgW+OGhjjJ3B3RK5mUio1OTnKjAx2nz6O19auxWBae1THKulwJK2VNNbVUXx8nBpA8zKYWk1id+QHuV0vGoKi9LAwexYc6lwuynJMJvzdOddlYn3iAP7cNyLhqoAPrVMHxCf3V3+8OKvvUMErn/bohBoRBLilV9AxI9ZsvVxaGcLnDooISQ0NCOFzMdvZaGerUKhcPzG+eaMuCMMtz9Q2EoqUyZWXS6vOFZVtv5qdGhr436tz3R2UK2iys8u+/lpz+zau1Zr3HpKkpVnu7fAXAGl8rmZvPY3XOO5gJU0iadKCjZIoSrSYfBMEcj+qDsSAkEiW7Q5afXjzxZ2tGm1Xu5bm2nwIIzRUW1xMmEwIiUQLDhY+/bRoros+8y7KcpY9cZnvS9t8cqBrmoO6rOGT/YZP9rueIVv/Ud5Xr99669tejtbouhSnczodDCZT1EdruQzaudfnJQWK2n4A1E0tHNQXAHCjombOtr0hH3xX8OErVNeuCetiBUuXyo4fZ8bFhSxfLpw2zd3hQO6hvnSpJi2t/LvvxDt2JBw4gNq5W4UDXDH6+JMXs5he5E9+TXJBW5BH6DPI+921CXeuKY/trnK4Mpdccew8fb/cFMBlZ7+7BKY4EAAgKUiU/e6SAC6775eb3B2LE+UuWKDIyIheu7bX7t0wxXmYsfv1i16zJvXGDRKDcWP4cBe06PQsp7pUW3hb/dLHnXFtAupGwmK9Boz2Tf+j0uGaXNaX0wkN/Xj6UoVCdfHNBY5XBXUnF99cUKFQ/Xj6krsDcQpxWpoqMzNu61b+SLhxG3RPr337SBzOnZkznd2Q07OcQ2kVPiJaaAzL2Q1BHmfBuzEKqaG6tMGxajypL2fzhWvj4iLdHQXUFY2Li9x84Zq7o3CKqs2bBRMnsnr2dHcgUNcS9e239Tdv6oqLndqK07OcgtvqmEQ41hhqAYoCLw754vE6x6rxpHE5JVLF4iGpjtcDdT+Lh6SWSBXujqLzaYuKjLW1kZ9/7u5AoC6HFR9PCwio2rjRqa04PctRyQ2RPb2c3Qrkofi+tMrih6UvJ79WiuH4sCi4GzPUgmFRoRiO59dK3R1IJ5MdPkzm8QAKV6CFWsDu27f+5k2nNuH0T57JSPiI6M5uBfJQTDapXm1yrA6PmUZeUCsjk+BvPdQqMgktqJW5O4pOpi0ooAqF7o4C6qJYvXsbZc79zLviNxfHPOM4ZNDhP3989+PFWWql0c6H1Fbpzv0j6VhzVaXaN56+ciZd3LGH20OtNH68OOuvDaW2wihp+Pnjuy9NzHxuaMZb06+m/1Gh12HOC6kJBDg+Fdxj+nJwHMQIH7r13yD7xQgFXWz9wk5gUipJXDhoAWoZLSCAMBic2gQ8s+w4tdK4/uO71WVadwfScXeuKj57Kbs4Rz11UegbX/dMHSH4+9fyHz/IU8ntzfO6AM/Ioc086Ez9eF6R1xufrz972c7yOEHsz8rNLKnoWHPL9hxJXr1BotZ07OH2yK6ShK783sYzMmL47ut3Ur7cyH3rC+5bX6R8ufFAdp4rV4guqJW7rC2XITxq0c6Sjz++NnhwQ16eneUNEok4LQ3XduQogGu1d1988e6LL3bs4XYSp6XZfkb6ysqid9+92r//pZ49r/bvX7xypUHSwVP3DiAwzNlrnrliESqpWO+CVhxHpaOLV8a4OwrX0Wqwf3ZWBYQxX/ooludDBQAk9OPF9uaueS/nyqm6kU86uti5XTph5WLEsxKd7urk3eJZW/f+vfAZdwfSQUYMf+/giXVnL/cPD9o44zEAwKbz1+Zs2/vKsEf+N2E4GQ4rgZrBtdqSDz8EAPhOmeLuWDqoIScnb8kSXKsVPv00OzlZfe2a5M8/lefPx/78MyMqyjUxOPvn21aWYzJ2TuepCzaDhDpApTDWK43xfXnmFMcsIt4rNMbrbrZ68Hg/Kt3pv+zKOsf7Kj0mxalQqBDEY66vjY6NqP/mXXdH4TrXyqt+y7yxcFDf1Y+PoZBQAMBTveNf+evwrxdvPNU7LjnYJUl/d4RpNB60B0vYypVhK1e6OwoXIQyG6l9/JYzGHr/8woqPBwDwhg/njxqVt2hRzbZtYR9+aN6Fw9PZynLIZI/5RQYAVJVqv3nr9qTZQVQaenBbRU25NizWa/wzgSnDBSTS/W1BCJCfrUr/o+LOVSWOEzGJnImzgnr25SEoAAAYdPgvXxZIqnSvrY5jcym5N5SbPs9f+llcWYHmUFpFTblWFMyYNDuo/2gfMgXNvaFc9eotAEDRHXX6HxXzlkcNnejXPCqCAGX59Qd/r7h5QQ4A6D2AP/nZ4JDollcPktUa/tlReelkrVJmFAUzRjwuGj7Zj0a/9zn7a0NpznWlOTbzLWql8bvlOXF9uNMW3Zu2I63RH/i9/MopKQBgwszAAWN8bbxifoH0Dzf2bvcL3am4AorDdXhMX04Qj+NZvfcPlTyJFAAwoWc05f4IcQoJndonPu1yVmZJhWuyHAIQCq2rNzB2NgRF4ce+a8I0Gn1VlVdCAiM83HIjMyaG3adP/bVrJoWCInD6OEJNdjbq5I1Nutu2Kf/+VaVrwEY+4R8SxTp9SLz+o7wny0MmzQkikRCCAOf+kfz2dUFcH+6Cd6PJZORMuvi75XemLgwdOy3AkglZM+rxnetKdFrsieeDKVT0TLp48xf5SplhwsygwDDmy5/02L2xNDSGNXSinzCwhXlklhb/396dxzdR530A/00madKkaZLe9wG0peUuICIgVUEQUVjUFVEQRRHYVdZV1PXRddVl1UVdcZVjARF9QLx4QBFUFuQUucql0HKU0itp0/Q+cs3M80faNGnTNG2uZvp5v3j11bST33xnOmS+8zuT00MWvDCAELLrs9J3lv229B+ZHVfkLr3W9O+X8jiWmzYnIS5FejG35qv/XM8/Uzt/2YBQlUupgLqoeeVfLppM7D2PJ4dHiw/sLC+42GDsZlfi8mJ9WWFTVrZCJA6UKnp8gDrwp6+/P3jl+ur773xzz5H9l6+JaHpK5oDX7sxJDVdZtzEx7I5zeW/uOXxZqxNQ1Lh+SS9PnXhDcrylvum/+QUz/7N1xczJiyeMbjSa5n2yLVGl+OPNNyzbvmf/5WuEkJy01BUzJw+IDLPsbv3PuYSQmf/ZGi6T7lw0Z0icg2E1DQbjvw8c++joaXVdQ2xoyKNjs5fmjJEGObi8OY78dPnaP348dKqojOW4wbFRf7l9wrRBaQKKIoScL6uYvmbLC5PHLbaZfGj1oRNv7jliu+uTRWV/27X/SEGRXCJ+ecrNI5PinJyxeTcMm3eDn5N+ihBlMN9GpAqCg32zI7a5+cqf/0wIiZ47t+T995suXBBIpREzZsQ98YQoLMy6mbm2Vr1+feX27aaqKlouj5gxI3bBAusosMLXXqv68ceBGzZIMzKa8vPzFizo/8YbrMFQ/M47+qIiWi6Pnj079vHHaZnM8ltzdTUh5OSoUVH3399ZJZCxokK9YUPljh1Mfb0kKSn2scciZsygHN3aObO5as+estWr9YWFhKLko0YlPPlkyLBhlsU1a48cyV+4MOM//1GMG2d9S+Frr9UfPz5w0yZLRsKZzZU7dqjXr9cXFcmyspL/53+cnDGhSpVls1qqX8iGDGHNbg6z7QLfspy6KtMfXsvIGqkkhAy+Qfn5h4U/faPJnhCW2F9WUtD49frrN06KnPt0P0kwTQgZNla1dVXhzk9L0oeE9h/kYN35umoTw3BPLR+oDA8ihAwcrvjwr3kXcmtvmREjV4oyhodK5cKIGMmgUUqHwWjL9N9tLhk1MfyR5wZY9pg+NHTlXy7u/7a8XXWOvpnZtqGIEPLUPzLjU6WEkMGjlQMGh37417xDu8qnzUnosqGDYbgD32pMJvbpt7ISLCXcoPy/DUWnDuqGjQ3r4s02Yez5uoymqRHjwnzUtIJ+OV5TUlP3+4++SosKWzN7eqGuduX+X365Vrx94exBsVGktRvK2iMnJw/sv2zSTU1G878PHLtr7ZYVM2+fd8Mwh3/9w1eLfrh45faB/T+ZN+vE9dL1P+fetXbLNwsfSIsKnz9meLRctvyHQ/8zZcKopPgklYM1wEtr6+/b8MVFjXbeDcNuSU/dk3f1zT2HrlZWffD7aRL7T3yOI2uPnHx+x54RCbEr771DLKTX/Zz74Kavl9027vnJ40Wujcbfk1fw4Kavo+Wyd2dNFQvplfuP/ZB3levOpcJxZP/lQlpADYj02ci4QKo+750azp6tP306dPTo2Lffrj91qmLr1obc3PRVq0SRkYQQY3n5pSVLDKWlkTNnykePbjh7tuKLL2qPHEn717+C09IcFli2bp2+uDj24YeD4uJ0336r/ugjfUlJv+XLxXFxqa++Wrp6NSEk7vHHxYmJDt9u2/FFNnhw5Y4dha++aigrS/jjH9ttyZnNxW+/Xb5li2L8+LiFC1m9XvPxx3kLFiS/+GLkrFntVxF3hGOY4nff1WzapMzJSXj6aX1BwZVnn5VmZHTrBBo1msa8PNnAgbRU2q039lpez3IEAp/2Ph57e2TG8JZRizRNjbsj6pe92jM/Vyf2l535uZpluNvvi7MkHIQQoUgwYVr08X2VZ49WO8xyCCEjbw6zpDiEEKlcmJwekn+ujnFtbPyVX+u1av38Zf2te1RFiofeqLqW19BuEFPZtabfTtbMeizJkuIQQiiKZGUrht2oOvNzdc5dMbLQLv5SdVWmC7m1I8aFxSYFWw9/9C0Rro9UN5vYXZtLj+2tvO+J5I5VTV5SUtDodooSMCmOj/vlNBlNOWkpG+bcLZeICSG3pKf8bt3nqw6dWHnvHUKBYPeFy2uPnHxi3Kjld91myRtmDM14YOPXb+45dFNqQpqjEe+Xtbq3Zkx+YtwoiiJ3DU7vHxH2hy++O15UlhYVPjwhprKxiRAyOjl+Uka/ju/lOLL60IlL5bpP582aPjidEDJjyMCUcNVbPx6ed8Owm+1nSjxdon79+4NTMgdYg58xdOBTX+5+/8CxnLSU8f2Tujz2er3hvf2/pIQptz1+f4IylBBy1+D0eZ9ur2rsxmCWs6WaT4+fG5EQm53oo3VVBRRVUlPnm33xlbm2Nmbu3MRnn6WEwrDbb5dnZ19dtqzym29iFyzgGKb0ww/1hYUD3n1XOXEiIUR1223KnJxLS5aUrl3bb/lygdhBF1J9cXHGqlXSzExCiHLixKvPP99w+rRRo5EkJytuukn71VeEEOXNNzussmINhtK1a207vqhuvfXaK69ot22LmD49KMbuuqrZv798y5boOXMswRNCVJMmXV66tHT1anl2tsSmUakzTXl52m3boh54IPmFFywlKG6+OX/hQtfPHmc2azZvNut0YVOm+KwSztu83irBsj7tfZw0QGbb9hQWFRQRK9EUNzc1mDXFzeHRYlWE3Trvlg0qyvRGveOu1qqItuApighoSt/I6JtcagYqLWxSRgSFqtr2SFHknseTn31nUFiU3TnRFOv1TUxCql3uLJHSscnSKq2h1oVx3dVaQ0WpPrGfg8N3JVSjgf16fdE3nxTPmJ84dXa8z+7FCamyhH5uPjEE0uOvL7McuTho2aRxliyBEJKdGHvnoLS9+deuV9UYzMyXpy8kqRR/nHiDtWokTBq8eMKo4uq6A1ccT7CUpFJMG5RmPYIRCTFhsuA61/qR6Bqbfrx4dWJa8i3pLR/WFEWmZg6IVYScLlG323j7uTyWZW2DlwiFj48bSVPU9nN5ruwur0KXW1R2z/AsS4pDCJFLxA+P6UaD1PWq2iVffNdsMv19+q3hMh891DIcKxbxob+nLUNpqaHU/UV5XSVOTIyeN8/aHqS46aaQESOqfvzRXFtrKCmpPXIkbMoUxfjx1u1Dhg1T3Xpr/fHjhuJihwUqxo4NTm8ZeysQi+XZ2azBwOpduuwNxcUNublhU6fKMjMtP6GEwvA77iAM03z1qu2WnNGo27UrKC7ONnihUhn94INGtbru2DFXdld76BBh2Yi777aWIM3IUN58syvvJYQQlq347DPtF1+obr897I47XH2XexrPn6e8PIAxUPpeuCoixkFGxZg5liGMmaMEFNXNI5Yr21eiGAyMyejh+gOzmSWECBz1DXKR0cjqm5juHp1FcyPz6b8KfvyybOYjSXfNTXDYRanXYhiOMQdGdU6CMpT14aRvsQp5sqptNjahQDAiIUZdV19cXVevN+SXV/aLCAuX2j2u9Y8IC5dJf1U7ni0jTBosF9um7BRFKHVdgyvBqOsayusbE1UKmU0vnCFxUedfXLI050bbLZuMprzyyujQkESlXbNXskoRq5Bf01U3GrtO+i9V6OoNxmEJdgMCLEfnSrRXK6vu2/DFFW3Vprm/c6XqyFMoQkXK+LawsSQ+Xhwf77PdBaemilRtnc9ouTy4f3+jWm3Sai1fpQMH2g4domhalpVl0umM5Y6rvUUREXbbCwRsUxNTX+9KMMbycpNOF5ySYtvepBg/fsTBg6pJk2y3ZBoamgsKJElJQqVd/wdJcrJQpWq6dKnLfXFGY9OlS6LISHFsW095y9G5EipnNqs3bixasUIxYULq3/7msFrLG2RDhnBe/lTkW7+cSo1d6xjHEo7lxMECWkjRQopjOa5XTi0qFApI9yeJNhs562j/oCCBREr34Ojqqk0fr7hy9pfqB59MnXh3jK9THLc71dBCKnDarHyqXUphwbCcmWVZjjOzrFBAudLYbxURIg3q0GWyTu/h9miOEIZlBRQlcKPey1LD1LNJbnKL1Q9u+rq22bDxoZlTM300ZQh4iigqSiBpX4HNsSzHMJZ/3a056Lg8BWswsAZPX/YcR8xmiqZ7XN3LMQzb3ExRVA+WDGMNhpL33ivfvFmZk9Nv+XI6hFdLT/KtLqeiVG/bmbW60qgrN8SnSoNldExisLqouVJtV9NYVWGsVOuj4iTemBsmOkFSU2msq7abEubAt+V/XXCm+IrdHK8xiRKJlL56we6xWN/EqK83hUWKFa1jrMwm1mxTjVRXY6ppnW9GFSmOipcU5NWbTW0b1NeYa6ucTUhTozP+5++Xzh+vmf/sgFtmxPq+FkenMQTKpJEBp7i6tl21B8NyEqEwWCQUUJRQIDCzXC+cyIQihBYIWI7r7qTDdfq2Sz00WEIIMXf/GXHfpWu/W/e5mWG/XHDfnTbNc9BjPr7CTBUVds1JHMcxjEAsFkgkFE1TNO3tmoOeoSiKCIUcw3Rr1D3HMNaDpWhaEBzMcRzp5gEyDQ2Fr76q+eSTyFmz+v/zn7TcR50yfcbrWQ5F+bT38ckDurLCljWuzSb20K5ykVgweLSKEDL8JpVESu/drtE3M7YbGA3s0BtVnZbYFbbzy3LgcEVkrOTQrgrrHpsbmbO/VIfIhe365cSlSgeNUh75vsK6QDfHkQu5tWd/qR46RiWVCwkhinBRRaneejIZhss9pLP2Yg4NEw0fG3Zyv+7SuVrrBsf2anWaTk8+w3C7NpdePF0778/9x98R1bPWLjeFR4sxaaSX6Bqby20WTDCz7Lmy8lhFSKJKIZeIM6Ij8ssrNfV2ifXVyipdY9PgWM+vrRgll0WGSNslXpq6hjFvr//zth+MTFtHN2mQaGB0REl13dVKu9UwrlfXqmvrU8NV1goq27TGzLK226dHhcvFQT8X2PW00NQ11DidSv/7i1ce2rRNGiT8/NH7xvXzXUOVFcdxGteaQgKLL3NFQ2kp09h22TMNDfrCQnFCgigsLCg2VhQZ2XDmjO3CSRzDNF64IAoPD4p2MOGZm0QREUKVqrmw0PZxoikv7+zkyeqNG223pENCgvv1a7561aTV2v5cf/26ubpamt42Kb/t0bFNTdY+T1RQkDQ93ahW66/b9KvjuObCQicRMg0N1156SbdzZ8y8eUl/+UvHajBvazx/3tvPWl6/s3GcT3sf1+iM77948advNKcO6v79Ut5POzQz5ydZhh0l9JPd81jyL//VfvBS3rF9ldYN7pgdn5rZkwo6kUigDBOdPlJ1fF+lVu2gM1pknOTOBxN++a929d/yTx3U/fJf7Ypnfss/Uzt9bkK7MVOSYHrWgiRCyPsvXvzhi7Lzx2u+XFu4+m/5mSMUE+6MsjxQDh6tkoUKV7+Wv2+75tRB3fp/XD59pCq5dUQ6TVO3/i4mob9s7euXLJMQbvznlR++KKM7n9qx6HLjkR8q5EpRWWHT9o1F//dR27/Duyts64S8yd29sAzH9MZnMwd8/FCra2z69PhZU+vZOXG99LvfLk/JHBCnkIuF9H0jstR19WsPn7JuUNXUvPrQydjQkHYjnrqls+qTqBDZ1KwBh64WHbTp2nz0WnFeuXZYfEyQ/RSrM4cOFIuEK/cfq29tDtObzeuOnDIx7N1DMgghimBxqCToXKlG3zrTxm9q7U+XrllLyIqJvKlf4mcnz58tbelsUa83rPv5FMN2+ke4XlX73P/tkQWJtj5yn79mOqYoKoZ3T9L6goLmggLf7a6wsOqHH1punBxXc/hww+nTYZMn03K5OCFBMW5czYEDtUePWrdvOHu2et++kOzszoaCd8lJBYwkJUU+alT13r1Nea295jmu5uBBk04nGzTIdksqKCh82jSTVlvx2Wdc61Vtrqkp37xZFBUVOmYMIYSWy2mZzDYtqM/Nbfz1V2shyltuEUil6o0bmYaWp5emvLzqvXs7D50r37Klas+e6DlzEp95xmd9cWzJhgzpVrt5D3TZLyfAamzvX5JimftYq9anDw19ZkVWZrbScg4pioy/Iyo8OmjXltL1/7hMCMkYFvr0W1nWuY+7SyKlp86O/+Tdq6tfzb97XuKsx9o//Fn2mNBPun1j0ZrXLhFCho1VzX+mv8O5j+NTpcveGfTt/5bs2lJSW2VK7C+7d2Gy7dzHccnBS5dnbv+4ePP7BZJgetzUqKX/yLRdb1wZHvTk3wf+8EXp91tL9c1M5gjFkr9lbP/Y8cABQkjx1cbGejOpJ7u3th8BceOkyBtuiSCiAPjrC2j3Z9zxER+fTVpAfXT0dFF17eyRg3OL1et/zk0JVz5961hLb5U7stKeGDdq7ZGTBbrq+0ZkWebLuaar/ueMyWk9mh5GESyRi4M+PHiC48hNqQkK+9ntKIosnjB636VrCzbveOym7NHJ8f/Nu/rJ8bNTMgfMGjawXVEjEmJfnnrz8zv23LX2s0fHjrDMl3PieumSCTeMTU0khMQp5FMyB6w5fNK4aduDo4de1Gg/Ono6NUKVX66zlCALEv31jpx7138+a93WpTk3RofK1v2cm1ukpgWd/hE+OX6mQFedGRO5+cS5zfYLek4blJaTltKDcwKEEEk/BzMLeJFAUPzOO81XroSOHVt39Kh22zbFhAkRM2cSQiiajv/DHxp/++3q88/bzpdDh4TEL17cg3s8FRQkVKmqfvhB++WX8tGjO/bzxc7VWwAAFspJREFUFYjF8U88kb9kSf6iRbGPPGKZcafmwIHoOXPk2dmcya5BWZmTEz1nTvmWLfqiovBp0yzz5eiLi5NfeEGSkkIIkaalyUeO1GzaZK6tVYwf35Cbq9u9W5qebu0KLU1Pj1+8uOitt/IXLYqePdtUWaneuJGprxd0MvONvrhY+/XXArHYVFVV/Pbbtr+iFYqYuXP50UHHWZZTrfXueujeQNPUuClR46Y4rnKnKJI1UmmZM7Cjdqt1Dhyu+PjguHbb3PdEsnU5BUJI+tDQv388wkk8FEVSB4Y8/ZaDXu5xycHvfDHK9ifhMeL5z/af/2z/zkpLSpM9tdzultBuedGQUOE9jyXf81hbhE4a426+M9rhqhTAD/0jwixzHz/yv9uDRaIHRw99btJNkSEtGbaIFiy/67ZBsVH/PnBs4WffioXCG1MSVt8/3Tr3cXcNj49ZesuN7+47evBK4Y6FD3RMC+IV8h//MPftvT9vOnb23X1HY0NDnps03uHcxxRFnhg3KjlM+daew0u/2k0IGRYfs/nhe6xzHwsFgtfuvCVcJl19+MTuC1f6R6j+dc9UsVD42JZvrIUMi4/+8Y9zX9r50yu7fiKE5KSlfvTQjKVffe8w+Eaj6UyJhhByUaO9qNG2+21quLLPZjnup+YURflyhQfF2LGWuY+1X38tDA+PW7Qo9pFHrA0xQdHRGevWla1dq/vuO82nn4rCwtrNfdwtFE3HPvJI8+XLRf/8pzInZ8A773Rs8ZFmZg76/PPSDz4oXbPGMvdxyiuvWOY+bpflUEJh4rPPBqelaTZtKnjxRUokkmdnp/7979a5jwXBwanLl5etXq3dtq1y+3ZpVlbaypWNv/5asXVraxFU9Jw54oSEkvfeu/r887RcHv3AA4LgYM0nnziM31BcbCgpIYTovvuu3a+CU1Ojfv97fmQ5hOvcG0+ee+PJc042cMVjt/2ce0jnZiGuKC1s+vN9Jw7s1PhgX+Ap7l9jbzx13v2r1BVhz7218/wld0rYef5S2HNveioe55Z+tXvEm2vK6xp8szvwCPevsbDn3nSzBFtVe/eeGD7czUIuPPzwhYcf9kg8zjFNTfmLFuUvWsQ0Nflgd+ARVXv3HnfvGuuyBK/3y4lO4NuyLOBJGMQCAABe4/Usp6JUz3be4w/6OMbM0kK+TWfQGTPLBEgPIvAPjuPMbPeW1/Uqk1bL4ZIFLwtO7vlwB1c4v8F44DlbHExbh3Z7laWbCzqaBJa6arN1mbBejha4u8ZQnDK0BzO4QN9hZtk4pYOFTrul2dT13NAuEkVGur8mCSUUcl5edBoCF8cw+k7W1nCRUa2maGfrojgfY+WBLD48WnzpHNafA8eqKw39svwzare7GJZLcO8ONDopjuO4kpo6N8txxXv3TPX2LsCzSmrqOI4bnRTnZjnBova9uf0rKDKy4exZH+xIEBycvnq1D3YEHqQvKHBzWdCg2FjCOKsB9XpjwdAbVVd+4+E8V+C+S+frTAb2tt8FRpbjEeEy6apDJ7reDvqeVYdO+GxZUF+SDRtmrHC8MhpAfW6uOM7dzN55fYzXs5xZC5JMBnbXlhJv7wgCzuaV15LTeTFS0WVTswZsPfVr19tB37P11K9Ts3rdslnu1+dHz57N6vX1ubkeiAZ4p+HMGeWECV7dhdMsx0PjXybeHbNtQ7F17QIAQsjnHxYWX2lc/EqGvwPxqTWzp9fpDU99tdvfgUDv8tRXu+v0hjWzp/s7kPY8cg+QZWUVvfGGJ0oCXlFv2MAaDPFPPunVvfhieMvcP/UbMFj+ymNnUaMDFitfvLj789LfL07pgxMNvDVj0kdHTyPRAaunvtr90dHTb82Y5O9AvKX/ihWNeXlFK1b4OxDoRZqvXCn54IOoe+91vyjnuXiXKzx4xgsrB3/6XsG29UXffloyYJA8fWhoXIpU0Pls6x6nKzeER4stX90symRiRaKeZIf1NSa5sn3HwJ6FVFtlVIR1Y2iSdS8uvpFz+RnOxfivX26MjBMXXW7MP1NXdLVRrhD+6Y3M4ePCXNtJryCgiJtjrCwWjM0mhDy/4787f700e+TgJRNG+6AzMvRCJTV1qw6d2Hrq1zq9YeW9Uy0XBi9JkpOTnnmmaMUKw/XraR984O9wwP/UGzaUfPCBfPjw5Jdecr805+2qPspyCCFz/9Rv7p/6bdtQdO6X6h+/Uhuamah4SXmJg0UuvUEgICxLKAHh3B7Jy3E9bMpz+EZLYN6OgeM4y4jQHgffGRdPKctwwiCBNISOS5EufDHtxsmRngzCJ1iOeCodWTA2e8HY7EVbd245cf7f+49RFCUUCCiK6jK9pAUU49LlYlcULaA6X6Wy0z227otq3Yy413xhWwJHCKEFAidrZxJCBBTFtkzWYruZbQwuZeOODp9rPUXWMrv+vsPJpxyV4xKO48wsy3FceIh0auaAXthQ5XEx8+dLUlKuvfLKiaFDpRkZ8tGjpQMH6ouLZZmZXtqjUa0Oiu16ZEOXm7lYTpdMlZWiiIgeFGvUaIJiYnqwR8e7YFkicPyU3t0j7e72HMPoCwrqc3MbzpxhDYaoe+/1SIpDekldjtWsBUmWxbcB+jjrve1EUVlZTZ1Q4GzKB4uejUJ38q5mk6mzgcfuj3g3mBmxsNOD6rJ8Tw2571iOkWGCnE6w4dV4CCFmlolThro/aNzrzGYPzgmozMkZceCAbufOyh07qr7/Xvvll6zBQGi6u/lmZ9rf6miaY5gu30u1btbpll0txOVqzPaPmJRAwNk/sXQaZ4dn045bdsy4bTez/a3DLduF1Fn5Hd7QcmY6K9/2CSk4OVlfXCwIDhbHxcU89JBn++L0lrocAHBodFIc6f03POiDhJ6/QYRPnx4+nf91V9B79JXJ9YG3MAM9AAB0AlkOAAAA8BOyHAAAAOAnZDkQ4Dw7ZgwAAHgEWQ4EOPTLAQCATiDLAQDgD8b9OcEAeARZDgAAb1A0hU91gDb4/wAAAAD8hCwHAAAA+AlZDgAAAAQmk8n575HlAAAAQGDqZCU+K2Q5AAAAwE/IciDQYVZAAABwDFkOBDgkOQAA0Amhk9811psxsSz0drhCAQCgE86yHFkI7bM4AAAAADzLaYsV1kEEAACAgOU0y0FzFQAAAAQsZy1WhKKQ6EAv19hg9ncIAADQSznNcgB6PVmIED2QAVrh/wKAHbRYAQAAAD9hvhwAAADgJ2Q5AAAAwE/IcgAAAICfkOVAoEPvMQAAcAxZDgAAAPAT5suBwFatM6E6BwAAHHKa5SDFgV5PFR6ELAcAABxyluVUVRhx9wAA6KNY1t8RALjLWZYTFi1GdQ4AQKDgCCmvb/BYcQJ03ITezqhWUzTtZANcxAAAPEEREi0P8XcUAL4TFBvLMYyTDZDlAAAAAD85X8fKV1EA9HocIdqGRn9HAQAA3eA0y6F8FQVAr0cREhki83cUAADQDWixAgAAAH5ClgPgIrTgAgAEGGQ5AC5CCy4AQIBBlgPgEooiJTV1/o4CAAC6AVkOgEs4jiQoQ/0dBQAAdAOyHAAXoV8OAECAQZYD4CL0ywEACDDIcgAAAICfkOUAuAgtVgAAAQZZDgAAAPATshwAAADgJ2Q5AAAAwE/IcgAAAICfkOUAAAAAPyHLgUCHoU8AAOAYshwAAADgJ2Q5EOgwJTEAADiGLAcAAAD4CVkOAAAA8JPQye/qqozo2NkzddWmze8XnDygGzY27MEnU8NjxP6OCAAAoM9xVpcTqhIpVCKfhcIbDMPt2lJybG8lY+ZyD+l2bCo2m1h/BwXgNxc02vH/+kix7I0Fm3doGxr9HQ6A13Fmc9maNSdHjjw9cWL1vn2EQ42B36DFyvP0jcy1/LaP8pJrTc1NjB/jAfCjer1h2fY9Z0o0DMt9nvvbyv3H8IEPvFezf3/pqlWsXm+qrCx8/XV9YaG/I+q7kOV4nkRGp2bIrC8TUqXBUtqP8QD4UVlt/aXySuvLixptk8nkx3gAfKD+1CmOaXm4NVVUGMrK/BtPX4Ysx/Nompo2J2HMbRG0kMqeED7j4UShCOcZ+qg4hTw9OsL6MjMmUipCOzjwnHzkSIpuebgVRUWJ4+L8G09f5qz3MaEotCb2TKhKtPiVDH9H0TdQmP24V5NLxCtmTl742bfny8rvHZ61NGcMhRmOgO+UOTnxS5aUrVtHh4SkvPyyJCXF3xH1XU6zHAAAt2XFRB5++lF/RwHgO5RQGLdoUdyiRf4OBNBiBQAAADyFLAcAAAD4yWmWg045AAAAELDQLwcCW1ODGb2PAQDAIWQ5ENikMsxFBAAAjqFfDgAAAPCT0ywH81oAAPRVRrUavTMh0HVZl4NEBwCgLwqKjaXwrAsBrsssB4k8gAU+7gEAAgx6H0NgMxo5VKoDAIBDTrMcjsPzK/RyQUEC1DgCeAn+a0GgQ4sVAAAA8BNGkgMA8AQtEJTU1HmwQFTmQy/HsazzDZy1WNXVmD0aDAAAeBHDsgnKUA8WiMp86OUoQReVNc6ynFClCBc5AECfhbocCHTOkyCkOAAAABConI8kp5Do9ExdtWnz+wUnD+iGjQ178MnU8BixvyMCd1GEeLbHQ99xQaNd+Nm358vK7x2e9eaMSZEhMn9HBAB9BXofex7DcLu2lBzbW8mYudxDuh2bis2mLrpHQe/HEeLZHg99RL3esGz7njMlGoblPs/9beX+Y5jeCHiPM5vL1qw5OXLk6YkTq/ftw5xefoQsx/P0jcy1/Ebry5JrTc1NjB/jAfCjstr6S+WV1pcXNdomk8mP8QD4QM3+/aWrVrF6vamysvD11/WFhf6OqO9CvxzPk8jo1Iy2OvmEVGmwlPZjPAB+FKeQp0dHWF9mxkRKRSI/xgPgA/WnTnFMy8OtqaLCUFbm33j6MudZDvrX9wRNU9PmJIy5LYIWUtkTwmc8nCgUoc4s4HGE0zY0dr0d2JNLxCtmTh6eEEMLqPuzBy3NGYP1H4H35CNHUnTLw60oKkocF+ffePjN+SeK897HqMvpoVCVaPErGf6OAjyJIhS6zfZMVkzk4acf9XcU0G1GtZrQqIfuCWVOTvySJWXr1tEhISkvvyxJSfF3RHzmPFPBGCsAAHAgKDbW2uwC3UIJhXGLFsUtWuTvQAC9jwEAAICnkOUAAAAAP2GMFQAAAPAT6nIAAHiC40hpLWboBmiDLAcAgCcoiopXYIZugDaYLwcAAAD4CXU5AAAAwE/O5svB+mIAAAAQuJxlORSFNAcAAAACldMWK6w3AwEAuTgAQB9lVKspp+uQOM1y0GQFAAAAvVWX65CgLgcAAAD4yXldjq+iAAAAAPA053U5vooCAAAAwNMwXw4AAADwE7IcAAAA4Cdn8+VgjFWP1VWbNr9fcPKAbtjYsAefTA2PEfs7IgC/uaDRLvzs2/Nl5fcOz3pzxqTIEJm/IwKXsAaDv0MAcBfGWHkew3C7tpQc21vJmLncQ7odm4rNJtbfQfEYrtJerV5vWLZ9z5kSDcNyn+f+tnL/MTw9BQqBGI9nPcSZzWVr1pwcOfL0xInV+/ahysCPMF+O5+kbmWv5jdaXJdeampucjeYH4LGy2vpL5ZXWlxc12iaTyY/xAPhAzf79patWsXq9qbKy8PXX9YWF/o6o70K/HM+TyOjUjLY6+YRUabDU2cyMADwWp5CnR0dYX2bGREpFIj/GA+AD9adOWaeqM1VUGMrK/BtPX4Ysx/Nompo2J2HMbRG0kMqeED7j4UShCOcZ+ii5RLxi5uThCTG0gLo/e9DSnDFoCQfek48caV12QBQVJY6L8288fZnT3scUhUarnglViRa/kuHvKPoEXQU6SPZ2WTGRh59+1N9RAPiOMicnfsmSsnXr6JCQlJdflqSk+DuivstplgPQ64VHBfk7BAAAO5RQGLdoUdyiRf4OBNBiBQAAADyFMVYQ4NDLAwAAOoH5ciDAIRcHAIBOoC4HAh2FyxQAABxCvxwIcBQmPwYAAMeQ5QAAAAA/IcsBAAAAfkKWAwAAAPyELAcAAAD4CVkOAAB/lNTU+TsEgF6kyywH41cAAAJGgjLU3yEA+I5Rraaczu3XZZaDuUgAAACgNwqKjeWczu3nbLVOTAoIAAAAgctZloMFHgAAAKA3c56qOG+xQpoD0IIWUOjXCQDQ2zhvdsIYKwCXMCyHfp0AAIEFWQ6AS1CXAwAQcJxnOeh+DNACdTkAAL0Q+uUAAAAAP7nTLwd1OQAAAQQf2gB2UJcDAMAb+NAGsIO6HAAA3sCHNoAdZ7MCNjex+D8DAAAAAcpZlhMsxThzAAAACFRO8xgs8QAAAAABC7U1AAAAwE/IcgAAAICfnGY5HLoeAwD0UUa1mqJpf0cB4Bb0ywEAAAeCYmM5hvF3FABucV6X46soAAAAADzNeV2Or6IAAAAA8DT0PgYAAAB+QpYDAQ595AEAoBPolwMAAAD85GyFB0Ih0YHeDlU5AADQGadZDm4g0OthugMAAOgM5ssBAAAAfkLvYwhwyMUBAKATWOEBAhyuUgAA6ATqcgAAAICfkOUAAAAAPyHLAQAAAH5ClgMAAAD8hCwHAAAA+AlZDgAAAPATshwAAHAMs1FBoEOWAwAAjmE2Kgh0yHIAAACAn5DlAACAY2ixgkCHLAcAAAD4qcssB6k8AAAABKQusxx0PoPeDWuSA3gNbgAQ6NBiBQEOa5IDeA2eISDQIcsBAAAAfkKWAwAAAPyELAcAABxDezAEOoyxAgAAx3ADgECHMVYAAADAT0Lnvy660vjm0l/bXreNZ6EI4Vq/2uhsWC/X+qYeaWowS0Pah1qtNaoiRISibALhWmJo+QlHKKotZmtsnfxcV24IjxJ74+GFZTgBTVn2qys3hEeLW09bayS2Xy0H0xat/Wm2hscRQjk+Mz3GsYQSkKpyQ5glQts/MkXszli77ynKbGKFIoGDc9vxiBypVBsiYsU9iLnoSlPSAGkP3thdLMeeLCrr1lvq9IZQSU8OylZJTV2CMtTy1c2ibAvs7KUHS/ZU+UaGCaJpDxZoy1Kapq4+JlTuTjkVDY1RITInG7hyMbh/aCzrzrsBeMjZDfKF94e8+dT5trsUoWzut+3SnVZ2OQ/ni/pOzppA2e6bIxzVunf7GLjWn1iSIbufexvV4fxYv22XxVDtN+lwfF5H2X+1njdi/z1pSRMp61klNmkQZ71avHUlJKXJXlg52EuF2xocG/XeT78IBN04EI5wlNsHTgsohuUoinCc7WlsdwV0Yy8CAcWybW+3lO9mkC0xcRzVIZf1RPntrh9PXk6Wv1HrX8p5yc5+a19Cu6/WN3cRtu0Dgv2f2NXjFYvorjfqDtZkyps/3xqN7Tly8Ohq+etzXNv3Fh1/4jJTTY1QoaAoims9NZzJRIlEbsVgO/0ERbFGo0Ak8t7MW6zBwJnNtFTaEm3HCDv7vpPIjZWVoogISzmW09Lx/53rjBqNKDra+tKk0wWFh7f92v6itPu5tQStVhQR0XJtUFTb5wDXUvdAdXhLOwaNJsgmBvv9dHFo5upqwjBONuiiGuCF94c43wCgjzj09KP+DgHAp1S33iofNapjQt3xpteagNg8AHl5Iiuuw8vWCn2HT4/OfsJZMgWO61hpzjn6vrsHxrXe7DuGwdnEQ9mGZ5+H2T802rFkFXbluJ2uOQy1w0btz3PLE23LuXT0QN+jS4Lr6l20Uhn90ENONqC6LAIAAAAgEGEkOQAAAPATshwAAADgJ2Q5AAAAwE/IcgAAAICfkOUAAAAAPyHLAQAAAH5ClgMAAAD8hCwHAAAA+On/AY59vTl95LAPAAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "6a4b72c1",
+   "metadata": {},
+   "source": [
+    "**Point cloud:** A point cloud is a collection of points in 3D space. Every point point in point cloud may has multiple features associated with it. An individual point in point cloud has `(x, y, z)` values to indicate it's position in 3D space for x-axis, y-axis and z-axis respectively. And it may / may not have features associated with it; Most common features associalted with individual points are `(r, g, b, i)` values indicating red color, blue color, green color and intensity measure respectively.\n",
+    "\n",
+    "![image-11.png](attachment:image-11.png)\n",
+    "\n",
+    "Generally speaking, a dataset is a collection of train, validation and test splits where each split has multiple point clouds within them. Model interacts and learns from train and validation point clouds during training phase but never interacts with test point clouds. Test point clouds are considered as out of bag samples and used during evaluation phase only.\n",
+    "\n",
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "**Note:** No two point cloud splits share a common point cloud file! This may cause [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)).\n",
+    "\n",
+    "> 💡 **Under the Hood:** Datasets are built using [BaseDataset](https://github.com/isl-org/Open3D-ML/blob/f424215be133b8c2571e66bbab8fc5c4f2aaa931/ml3d/datasets/base_dataset.py#L12-L100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d22689b1",
+   "metadata": {},
+   "source": [
+    "## 🐱 SemanticKITTI Dataset\n",
+    "\n",
+    "The points (positions & features), point clouds & splits are essential components of any dataset. These individual compontents can be arranged in many possible ways; **To parse them, we need dataset-specific classes**. One such class is `ml3d.datasets.SemanticKITTI` which parses [SemanticKITTI dataset](http://www.semantic-kitti.org/dataset.html).\n",
+    "\n",
+    "You may download the dataset by running script available at [scripts/download_datasets/download_semantickitti.sh](https://github.com/isl-org/Open3D-ML/blob/master/scripts/download_datasets/download_semantickitti.sh)."
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "db59d1b0",
+   "metadata": {},
+   "source": [
+    "The dataset is organised as shown in this picture below:\n",
+    "\n",
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60ab883b",
+   "metadata": {},
+   "source": [
+    "Here, the sequence folders `00` and `01` contain point clouds. Now to read them we use `ml3d.datasets.SemanticKITTI` (More information available in [API docs](http://www.open3d.org/docs/release/python_api/open3d.ml.torch.datasets.SemanticKITTI.html))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "618f253a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import torch\n",
+    "import open3d.ml.torch as ml3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9739b5cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VERIFY: Download to dataset_path?\n",
+    "    \n",
+    "# Read a dataset by specifying the path. We are also providing the cache directory and splits.\n",
+    "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
+    "                                      cache_dir='./logs/cache',\n",
+    "                                      training_split=['00'],\n",
+    "                                      validation_split=['01'],\n",
+    "                                      test_split=['02'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8eb9c25e",
+   "metadata": {},
+   "source": [
+    "Here you may notice that point clouds of sequences mentioned in `training_split`, `validation_split` and `test_split` go into their respective split group. The three different split parameter variables instruct Open3D-ML subsystem to reference the following folder locations:\n",
+    "\n",
+    "- `training_split=['00']` points to `'SemanticKITTI/dataset/sequences/00/'`\n",
+    "- `validation_split=['01']` points to `'SemanticKITTI/dataset/sequences/01/'`\n",
+    "- `test_split=['02']` points to `'SemanticKITTI/dataset/sequences/02/'`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0e2077e",
+   "metadata": {},
+   "source": [
+    "### 🗡️ Accessing Splits & Number of Point Clouds Within."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53999c8a",
+   "metadata": {},
+   "source": [
+    "Any split can be accessed using `dataset.get_split(split_name)` method where `split_name` can take any of the valued given below:\n",
+    "\n",
+    "- `training`: Returns train data split.\n",
+    "- `validation`: Returns validation data split.\n",
+    "- `test`: Returns test data split.\n",
+    "- `all`: Returs entire dataset by combining all three splits above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f88db995",
+   "metadata": {},
+   "source": [
+    "Access train data split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83a166e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_split = dataset.get_split('training')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c544b28",
+   "metadata": {},
+   "source": [
+    "Access validation data split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c99b3932",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_split = dataset.get_split('validation')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1542853e",
+   "metadata": {},
+   "source": [
+    "Access test data split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95ee76c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_split = dataset.get_split('test')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3747192f",
+   "metadata": {},
+   "source": [
+    "Access all the splits:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a128832e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_split = dataset.get_split('all')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f41aeed",
+   "metadata": {},
+   "source": [
+    "Check the number of point clouds in each split:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65fffc53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(len(train_split))\n",
+    "print(len(val_split))\n",
+    "print(len(test_split))\n",
+    "print(len(all_split))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7c26d04",
+   "metadata": {},
+   "source": [
+    "These are same as number of point clouds present in their respective sequence directories."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c70fa37",
+   "metadata": {},
+   "source": [
+    "> 💡 **Under the Hood:** The returned split objects are built using [BaseDatasetSplit](https://github.com/isl-org/Open3D-ML/blob/f424215be133b8c2571e66bbab8fc5c4f2aaa931/ml3d/datasets/base_dataset.py#L103-L148)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "737a611f",
+   "metadata": {},
+   "source": [
+    "### 🌨️ Points & Point Clouds within Splits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5870edf",
+   "metadata": {},
+   "source": [
+    "There are three important methods available for split objects.\n",
+    "\n",
+    "1. `__len__`: Returns total length i.e number of point clouds available in the split. This also is an upper limit for indices to be passed into `get_attr` and `get_data` methods.\n",
+    "\n",
+    "2. `get_attr`: Takes integer ID between `0` and `len(split)` (exclusive) as input and returns metadata (e.g. name, point cloud file path, split etc.) associated with the specific point cloud. Each integer ID is associated with a unique point cloud in the split.\n",
+    "\n",
+    "3. `get_data`: Takes integer ID between `0` and `len(split)` (exclusive) as input and returns point positions, features and labels associated with the specific point cloud. Each integer ID is associated with a unique point cloud in the split.\n",
+    "\n",
+    "Let us probe `validation` split as an example."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b484107b",
+   "metadata": {},
+   "source": [
+    "Maximum integer ID possible for `get_attr` and `get_data` can be calculated using:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d35892f8",
+   "metadata": {},
+   "source": [
+    "len(val_split)-1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818ac406",
+   "metadata": {},
+   "source": [
+    "Now, let us have a look at attributes i.e metadata associated with point cloud present at ID `0`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a8b35f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dictionary containing information about the data e.g. name, path, split, etc.\n",
+    "attr = val_split.get_attr(0)\n",
+    "\n",
+    "print(attr.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09af7cc6",
+   "metadata": {},
+   "source": [
+    "Atttributes returned are: `'idx'`(index ID), `'name'` (name of point cloud file), `'path'` (path of point cloud file), and `'split'` (split name of point cloud file)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72d02c29",
+   "metadata": {},
+   "source": [
+    "Now, let us also have a look at actual point cloud data at ID `0`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e10fd476",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = train_split.get_data(0)  # Dictionary of `point`, `feat`, and `label`\n",
+    "print(data.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "158a6796",
+   "metadata": {},
+   "source": [
+    "\n",
+    "![dataset_coordinates](https://user-images.githubusercontent.com/93158890/162549410-6369cbd0-b835-4216-ba54-945e3f591395.jpg)\n",
+    "\n",
+    "- The **`'point'`** key value contains a set of 3D point coordinates - X, Y, and Z:\n",
+    "\n",
+    "- The **`'feat'`** (features) key value contains RGB color information for each of the above points.\n",
+    "\n",
+    "- The **`'label'`** key value represents which class the dataset content belongs to, i.e.: *pedestrian, vehicle, traffic light*, etc.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c413c43c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#support of Open3d-ML visualizer in Jupyter Notebooks is in progress\n",
+    "#view the frames using the visualizer\n",
+    "#vis = ml3d.vis.Visualizer()\n",
+    "#vis.visualize_dataset(dataset, 'training',indices=range(len(train_split)))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/tutorial/notebook/03_train_model.ipynb b/docs/tutorial/notebook/03_train_model.ipynb
new file mode 100644
index 000000000..d338333a9
--- /dev/null
+++ b/docs/tutorial/notebook/03_train_model.ipynb
@@ -0,0 +1,547 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e7cd83b2",
+   "metadata": {},
+   "source": [
+    "## 🚂🚂Training a Semantic Segmentation Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "626f62dc",
+   "metadata": {},
+   "source": [
+    "In this tutorial, we will learn how to train a semantic segmentation model using either PyTorch or Tensorflow. \n",
+    "\n",
+    "Before you begin, ensure that you have the right dependencies installed in your enviroment. The requirements text files are present in the main directory of Open3D-ML respoitory.\n",
+    "\n",
+    "\n",
+    "PyTorch users may run:\n",
+    "```sh\n",
+    "pip install -r requirements-torch-cuda.txt\n",
+    "```\n",
+    "\n",
+    "Tensorflow users may run:\n",
+    "```sh\n",
+    "pip install -r requirements-tensoflow.txt\n",
+    "```\n",
+    "\n",
+    "\n",
+    "In this tutorial, we will learn:\n",
+    "\n",
+    "🎯 Downloading datasets available in open3D-ML. <br/>\n",
+    "🎯 Build dataset, model and pipeline from config file. </br>\n",
+    "🎯 Training a model using train and validation splits. <br/>\n",
+    "🎯 Evaluating perfromance on test dataset. <br/>\n",
+    "🎯 Running inference on dataset splits or novel point cloud data <br/>\n",
+    "🎯 Restoring weights from checkpoints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af472b00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a temporary working directory.\n",
+    "# Note: make sure you have sufficient write permissions.\n",
+    "import shutil\n",
+    "shutil.mkdir('/tmp/o3dml')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c61cdb80",
+   "metadata": {},
+   "source": [
+    "### ⏬ Downloading Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15151b22",
+   "metadata": {},
+   "source": [
+    "Open3D-ML provides [scripts](https://github.com/isl-org/Open3D-ML/tree/master/scripts/download_datasets) to download datasets locally. Let us download Toronto3D dataset. (We chose this dataset becuase it is small in size compared to other datasets like SemanticKITTI).\n",
+    "\n",
+    "Let us use the Toronto3D dataset script to download point clouds. Let use write the downloading script locally first:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7f719b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile /tmp/o3dml/download_toronto3d.sh\n",
+    "#!/bin/bash\n",
+    "  \n",
+    "if [ \"$#\" -ne 1 ]; then\n",
+    "    echo \"Please, provide the base directory to store the dataset.\"\n",
+    "    exit 1\n",
+    "fi\n",
+    "\n",
+    "if ! command -v unzip &> /dev/null\n",
+    "then\n",
+    "    echo \"Error: unzip could not be found. Please, install it to continue.\"\n",
+    "    exit\n",
+    "fi\n",
+    "\n",
+    "BASE_DIR=\"$1\"/Toronto3D\n",
+    "\n",
+    "export url=\"https://xx9lca.sn.files.1drv.com/y4mUm9-LiY3vULTW79zlB3xp0wzCPASzteId4wdUZYpzWiw6Jp4IFoIs6ADjLREEk1-IYH8KRGdwFZJrPlIebwytHBYVIidsCwkHhW39aQkh3Vh0OWWMAcLVxYwMTjXwDxHl-CDVDau420OG4iMiTzlsK_RTC_ypo3z-Adf-h0gp2O8j5bOq-2TZd9FD1jPLrkf3759rB-BWDGFskF3AsiB3g\"\n",
+    "\n",
+    "mkdir -p $BASE_DIR\n",
+    "\n",
+    "wget -c -N -O $BASE_DIR'/Toronto_3D.zip' $url\n",
+    "\n",
+    "cd $BASE_DIR\n",
+    "\n",
+    "unzip -j Toronto_3D.zip\n",
+    "\n",
+    "# cleanup\n",
+    "mkdir -p $BASE_DIR/zip_files\n",
+    "mv Toronto_3D.zip $BASE_DIR/zip_files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "610773c7",
+   "metadata": {},
+   "source": [
+    "The bash script takes path to output folder as input where dataset must be downloaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "775f199e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%shell\n",
+    "\n",
+    "bash /tmp/o3dml/download_toronto3d.sh /tmp/o3dml/toronto3d_dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78cd2f4b",
+   "metadata": {},
+   "source": [
+    "You may see downloaded point cloud files as shown below:\n",
+    "\n",
+    "```\n",
+    "/tmp/o3d/toronto3d\n",
+    "└── Toronto3D\n",
+    "    ├── Colors.xml\n",
+    "    ├── L001.ply\n",
+    "    ├── L002.ply\n",
+    "    ├── L003.ply\n",
+    "    ├── L004.ply\n",
+    "    ├── Mavericks_classes_9.txt\n",
+    "    └── zip_files\n",
+    "        └── Toronto_3D.zip\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a3cf348",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%shell\n",
+    "\n",
+    "tree /tmp/o3dml/toronto3d_dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1ec1929",
+   "metadata": {},
+   "source": [
+    "> ### (Tesorflow Only) Limit Memory Usage ⚙️\n",
+    "> \n",
+    "> TensorFlow maps nearly all of GPU memory by default. This may result in out_of_memory error if some of the ops allocate memory independent to tensorflow. You may want to limit memory usage as and when needed by the process. Use following code right after importing tensorflow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1bb347c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
+    "if gpus:\n",
+    "    try:\n",
+    "        for gpu in gpus:\n",
+    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
+    "    except RuntimeError as e:\n",
+    "        print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b955f11",
+   "metadata": {},
+   "source": [
+    "### 🧩 Build Essential Componets for Training\n",
+    "\n",
+    "First, let us do some basic imports. Import statements are depend on what framework you are using - Pytorch or Tensorflow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4dfca8e",
+   "metadata": {},
+   "source": [
+    "Tesorflow users may run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f10e33a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import open3d.ml.tf as ml3d\n",
+    "from open3d.ml.tf.models import RandLANet\n",
+    "from open3d.ml.tf.pipelines import SemanticSegmentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "720bd6ab",
+   "metadata": {},
+   "source": [
+    "Pytorch users may run:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bcaac2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import open3d.ml.torch as ml3d\n",
+    "from open3d.ml.torch.models import RandLANet\n",
+    "from open3d.ml.torch.pipelines import SemanticSegmentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2861200",
+   "metadata": {},
+   "source": [
+    "> The goal was to create `ml3d`, `RandLANet` and `SemanticSegmentation` based on either PyTorch or Tensorflow framework."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b941e6d",
+   "metadata": {},
+   "source": [
+    "Let us now define a config file with `dataset`, `model` and `pipeline`. Just add dataset path to the config file present in `Open3D-ML/ml3d/configs/randlanet_toronto3d.yml`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8319720",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile /tmp/o3dml/randlanet_toronto3d.yml\n",
+    "dataset:\n",
+    "  name: Toronto3D\n",
+    "  cache_dir: ./logs/cache\n",
+    "  dataset_path: /tmp/o3d/toronto3d/Toronto3D # path/to/your/dataset\n",
+    "  class_weights: [41697357, 1745448, 6572572, 19136493, 674897, 897825, 4634634, 374721]\n",
+    "  ignored_label_inds:\n",
+    "  - 0\n",
+    "  num_classes: 8\n",
+    "  num_points: 65536\n",
+    "  test_files:\n",
+    "  - L002.ply\n",
+    "  test_result_folder: ./test\n",
+    "  train_files:\n",
+    "  - L001.ply\n",
+    "  - L003.ply\n",
+    "  - L004.ply\n",
+    "  use_cache: true\n",
+    "  val_files:\n",
+    "  - L002.ply\n",
+    "  steps_per_epoch_train: 100\n",
+    "  steps_per_epoch_valid: 10\n",
+    "model:\n",
+    "  name: RandLANet\n",
+    "  batcher: DefaultBatcher\n",
+    "  ckpt_path: # path/to/your/checkpoint\n",
+    "  num_neighbors: 16\n",
+    "  num_layers: 5\n",
+    "  num_points: 65536\n",
+    "  num_classes: 8\n",
+    "  ignored_label_inds: [0]\n",
+    "  sub_sampling_ratio: [4, 4, 4, 4, 2]\n",
+    "  in_channels: 6\n",
+    "  dim_features: 8\n",
+    "  dim_output: [16, 64, 128, 256, 512]\n",
+    "  grid_size: 0.05\n",
+    "  augment:\n",
+    "    recenter:\n",
+    "      dim: [0, 1, 2]\n",
+    "    normalize:\n",
+    "      points:\n",
+    "        method: linear\n",
+    "pipeline:\n",
+    "  name: SemanticSegmentation\n",
+    "  optimizer:\n",
+    "    lr: 0.001\n",
+    "  batch_size: 2\n",
+    "  main_log_dir: ./logs\n",
+    "  max_epoch: 200\n",
+    "  save_ckpt_freq: 5\n",
+    "  scheduler_gamma: 0.99\n",
+    "  test_batch_size: 1\n",
+    "  train_sum_dir: train_log\n",
+    "  val_batch_size: 2\n",
+    "  summary:\n",
+    "    record_for: []\n",
+    "    max_pts:\n",
+    "    use_reference: false\n",
+    "    max_outputs: 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2178391e",
+   "metadata": {},
+   "source": [
+    "Now, let us load/read the config file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "031d8528",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from open3d.ml import utils\n",
+    "\n",
+    "cfg_file = \"/tmp/o3dml/randlanet_toronto3d.yml\"\n",
+    "cfg = utils.Config.load_from_file(cfg_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c71ada2",
+   "metadata": {},
+   "source": [
+    "Create dataset object from config file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "beb9aa91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = ml3d.datasets.Toronto3D(**cfg.dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b576e86b",
+   "metadata": {},
+   "source": [
+    "Create model object from config file: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f3d73b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandLANet(**cfg.model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17b07e24",
+   "metadata": {},
+   "source": [
+    "Create a pipeline object from model, dataset and config file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a965c22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline = SemanticSegmentation(model=model,\n",
+    "                                dataset=dataset,\n",
+    "                                **cfg.pipeline)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "352e623d",
+   "metadata": {},
+   "source": [
+    "### 🛤️ Training The Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c88c974d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline.run_train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1f412a7",
+   "metadata": {},
+   "source": [
+    "The training checkpoints are saved in: `pipeline.main_log_dir` (default path is: `'./logs/Model_Dataset/'`). You may use them for testing, inference and re-training purposes.\n",
+    "\n",
+    "Have a look at training logs and loss curves. Understand if your model is generalizing well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8a944ff",
+   "metadata": {},
+   "source": [
+    "### 🧑‍🏫 Model Evaluation\n",
+    "\n",
+    "evaluate the trained model on the test split by calling the `run_test()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d84b47cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline.run_test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d7d3ebc7",
+   "metadata": {},
+   "source": [
+    "### ✅ Inference on Individual Point Clouds\n",
+    " \n",
+    "`pipeline.run_inference` method let's us make predictions on novel point cloud data. It takes a dictionary with `point`, `feat` and `label` keys as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce851c07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input data\n",
+    "train_split = dataset.get_split(\"test\")\n",
+    "data = train_split.get_data(0)\n",
+    "\n",
+    "print(data.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce2653e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run inference\n",
+    "results = pipeline.run_inference(data)\n",
+    "\n",
+    "# Print the results\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce33b688",
+   "metadata": {},
+   "source": [
+    "> 💡 You may replace `data` with custom point cloud data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f99e7a15",
+   "metadata": {},
+   "source": [
+    "### 🔃 Restoring from Checkoints"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e916d4c",
+   "metadata": {},
+   "source": [
+    "In the above example, you may notice that we are using latest trained model to make predictions. Latest trained models may not always be the best model, so you may want to use model from previous checkpoints instead. \n",
+    "\n",
+    "`pipeline` provides `load_ckpt` method to restore model weights from training checkpoints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f017247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ckpt_path = \"path/to/trained/model\"\n",
+    "pipeline.load_ckpt(ckpt_path=ckpt_path)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 7aa40a8b2b9fa229a2ee0422be5b34b0ba881399 Mon Sep 17 00:00:00 2001
From: Asapanna Rakesh <45640029+INF800@users.noreply.github.com>
Date: Thu, 16 Jun 2022 11:21:27 +0530
Subject: [PATCH 3/5] re-write condensed tutorials based on suggestions

---
 docs/tutorial/notebook/01_config_files.ipynb | 797 +++++++++++++++----
 docs/tutorial/notebook/02_datasets.ipynb     | 138 ++--
 docs/tutorial/notebook/03_train_model.ipynb  | 212 +++--
 3 files changed, 868 insertions(+), 279 deletions(-)

diff --git a/docs/tutorial/notebook/01_config_files.ipynb b/docs/tutorial/notebook/01_config_files.ipynb
index de5fe2239..cee93b8e6 100644
--- a/docs/tutorial/notebook/01_config_files.ipynb
+++ b/docs/tutorial/notebook/01_config_files.ipynb
@@ -5,159 +5,94 @@
    "id": "e9b3f3ad",
    "metadata": {},
    "source": [
-    "# Config Files & How to Use Them 🚀🌍💫\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9a10a6f0",
-   "metadata": {},
-   "source": [
-    "Look at the code snippet below:\n",
-    "\n",
-    "```py\n",
-    "# Read a dataset by specifying the path, cache directory, training split etc.\n",
-    "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
-    "                                      cache_dir='./logs/cache',\n",
-    "                                      training_split=['00'],\n",
-    "                                      validation_split=['01'],\n",
-    "                                      test_split=['01'])\n",
-    "```\n",
-    "\n",
-    "The `dataset` object is created by explicitly passing dataset-specific parameters to the constructor of `ml3d.datasets.SemanticKITTI` class. Instead of passing these parameters one by one manually we may use config files to automate our processes. Each config file in Open3D-ML contains parameters (i.e key-value pairs) for `dataset`, `model` and `pipeline` in general.\n",
-    "\n",
-    "\n",
-    "> 📝 **Note:** We use [YAML format](https://en.wikipedia.org/wiki/YAML) for defining our config files.\n",
+    "# Config files\n",
     "\n",
-    "In this tutorial we are going to learn:\n",
-    "\n",
-    "🎯 Basic structure of a config file. <br/>\n",
-    "🎯 Reading & loading a config file into memory. <br/>\n",
-    "🎯 Accessing and mutating config file parameters.<br/>\n",
-    "🎯 How to build a dataset component using config file<br/>\n",
-    "\n",
-    "\n",
-    "> We expect you to be familiar with these concepts:\n",
-    "> \n",
-    "> ✅ Machine learning fundamanetals.<br/>\n",
-    "> ✅ Supervised learning fundamentals.<br/>\n",
-    "> ✅ Machine learning project lifecycle essentials.<br/>\n",
-    "> \n",
-    "> *Please note that we tried to present these prerequisites as simply as possible as they go out of scope for this notebook. Some of them may be too simple to sound technically correct so beware."
+    "This tutorial shows basic usage of config files."
    ]
   },
   {
-   "attachments": {
-    "image.png": {
-     "image/png": 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ayFoAAABpIWsBAACkhawFAACQFrIWAABAWshaAAAAabn7rPiamhpJ6wAAAOis3M1anU4naR0AAACdlbtZm5eXJ2kdAAAAnZW7WRsbGytpHQAAAJ0VxkYBAABIC1kLAAAgLWQtAACAtJC1AAAA0kLWAgAASAtZC+B5oigeOHBg3LhxFEWlpaXl5eWlpqauXr2a53mPLL+goODRRx/V6XRPP/10cXHx/fff/9xzz1ksFkKIXq+fMmVKWlqaR1bURmlpaVOmTNHr9d4uBMDL3L3mBwDcd+rUqUWLFq1du3bixIkURV25csVut3tq4VVVVX/961/79etXXl6uUCjKy8s5jvPUwgFACshaAM/bu3dvUlLSsGHDKIoihPTq1evw4cOeWnhmZubPP/+8ZMkShUJBCAkNDf388889tXAAkAKOIQN4mN1uLy8vDw4OlsvlUiy/rKwsICDAz89PioUDgBSQtQAexvO82WyWbvm1tbXSLRwA3MQwTH5+vpuNkbXQFdlstq+//nrq1Kk6nU6n082aNSszM7PRl6ZOnbp169b6J0Sd433y8/PXrVvXp08fiqJSU1OPHTsmiqLzVbVavWHDhg0bNqjV6oEDB2ZmZt46XslkMq1fv37EiBEURY0YMeL999//4IMPnI1dlJ2ZmTlw4MD777//7NmzzlWnpaVZLJann3562bJlTc1lsVjef/9957r69Omzbt06k8nUoA3P86+++uro0aMb3Pn84sWLycnJW7durVvLmTNn5s2bp9Pp+vTps2HDBpvNVjclLi5u3bp1ziFaAJ0ez/NRUVFuNkbWQpdjMpmef/75jRs3Llu2rLq6urS09IEHHjAajc6XlixZUvdSZWXlkiVLVq5c+fbbb9eP25KSkldeeSU5OfnSpUsGg2HMmDGPP/74pUuXCCH33Xef2Wx+6qmnnnrqKbPZfObMmcTExFsLWLx48d69ez/55BNBEPbs2VNeXv6///u/zVaemJh45syZTz/9dMCAAZcvXxZF8b777mt2Y1944YVjx4599dVXoiju27fv1KlTixcvbhC3DMPMmDHj2rVrZ8+erT99z549UVFRI0eOdP749ddfb9u2bcOGDZWVlS+99NLf/va3RYsWLVu2bMWKFQaDYcuWLZs3b964caNztwOg02MYxs2WyFroWkRR/PDDDzMyMj7++OOhQ4fSNK1SqWbPnj1kyBBCyPbt23/88ce1a9c6X2JZdsKECW+88cb69et//fXXuoWcPXv2rrvuuvPOO2ma1ul0Dz30kE6n27Nnj5s1bN++PT09ffny5QkJCRRF6XS6xYsXz5w5U4rt3b59e0ZGxvLly6Ojowkh0dHRzz333MGDB8+cOdOg5W233TZ58uT9+/fbbDbnlKqqqiNHjkydOjUiIsI5JSgo6MknnwwMDGRZdtasWXfdddeuXbuWLFnSs2dPiqKGDh06ZcqUo0eP4nHXAA0ga6Fr0ev1P/zww4wZM7p169bgpdra2t27d0+bNq137971pw8ePDgpKWn//v11U4YMGdK/f/+6H8PDw/v162cwGNzpz1ksliNHjkybNq1Xr151ExUKxe23397KTWqac4vGjRtXf2NjYmJiYmIuXLjQoLFGo0lNTT1x4kRRUZFzyqVLl65cuTJ58mTnaGpCSFJSUnBwsPP/Wq02ISGhf//+8fHxzikURSUkJBQVFVVXV3t8WwA6NGQtdC16vb64uLhfv351+VHHbreXlZXFxsY2OC6k0WhiY2MLCwutVqtzSkhISGBgYF0DiqIYhikpKalr4ILZbM7Nzb11LVJwbtHrr79O0zT1u5CQkN27d5eVld3aftSoUYSQY8eOEUJ4nt+9e/eoUaN69uxZ1yAiIkKpVLpeqclkcud9AOhSkLXQFTmvTG2USqVqhwLa84qd999/X7zF0qVLb20ZExMzadKkffv2mUym0tLSI0eOTJo0qX3eEIDODVkLXYtSqdRoNBkZGbe+JJfLw8LCLl++3OBOiiaTKS8vr1evXs126dzhPA2cnZ3d4ICzc3CWZ7Esq9Vq8/Ly3Lw3pHOE1Pnz53Nzc48fP86ybN2oKABoC2QtdC1RUVETJkzYtWtXSUlJg5e0Wu348eO/++67rKys+tPPnTtXUFAwduxYjxSg0+kGDx58+PDhutOihJCqqqr654M9RafTjRw58rvvvrty5YqbsyQkJMTExOzevXvPnj31R0UBQFsga6FrYVl20aJFLMs+//zzOTk5oihaLJZPP/301KlThJB77rln9OjRf/nLX06ePCkIAsdxP/7448svv/zSSy85Byq3HcMwDzzwgNlsfv3114uLiwkhxcXFK1asKCgo8Mjy66Mo6r777ouLi3vxxRczMzOdW5SVlbV8+fKqqipCSHZ29siRI+s/FCEwMHD69OmbNm06c+ZM/VFRANAWyFrocsLDwz/44IP+/fvPmTOHpulBgwadPn06MjKSEKLRaFatWnXvvfe+8MILDMOEh4dv2rRp7dq18+fP92DqJCQkbNmyxc/Pb+TIkTqdbsGCBdOmTXvmmWc8tfz6nBt7++23z5w5k2GYXr16LV26dPDgwVqttqlZ7rjjDqVSmZSUFBcXJ0VJAF0Q5eZV5ykpKYSQgwcPSlwPQBeVlpa2Zs2azz//vP61QF5x7dq1+fPnL168ePbs2d6tBMCXKZXKL774ws0r4919zk+jVwgAgEeIonjt2rUePXqEhoZ6uxZy+vRprVaLUVEAHuTuMeSwsLCwsDBJSwHosoqKig4cODBmzBidTue86TF1i2bvluwRJpNp165dGBUF4Fl4fi1Aezt16tSxY8fmzp0bHh7O8/yFCxfeeOONmJiY+++/n6Io502PvVKYIAjbtm2rrKy89957MSoKwIPc7deWlJTceo0EALRCSEjIlStXpk2bRtN0UFDQiy++OGfOnPfee6/u9odekZaWxjDM1q1bV61ahU4tgGdhbBQAAECLtWhsFK75AQAAkBayFgAAQFrIWgAAAGkhawEAAKSFrAUAAJAWshYAAEBayFoAAABpIWsBAACkhawFAACQFrIWAABAWshaAAAAaSFrAQAApIWsBQAAkBayFgAAQFrIWgAAAGkhawEAAFqjpqbGzZbIWgAAgNbQ6XRutkTWAgAAtBjLsvn5+W42RtYCAAC0GMdxMTExbjZG1gIAAEiLdbNdeXm5pHUAAAB0Vu5mbWhoqKR1AAAAdFY4hgwAACAtd/u1ANAJnMurOp9fXVxlNlo5Qrk9m9hwQlGVpVuQqtWz55Qae0b4tWh2QSB0va5BtdkeoJG7uwSXxRBCzDZOrWzVl2FjSyOEnMiuGN47pDULdHsVTg5ekLFt7TLllBp7hvu5XpEgijTt/l9Mq7gs4FY0TQWo5XGhmmG9QiID3f5r9BJ3/7xsNpsotvCdAAAfUKA3r9h+4WhGWUWNTSSijGFohhEJxYuEcvnl6eJFQRBouvVf8YIgHMqsdN3G9fe6KIqU6+pbssRbl9bGVOE47nKJudWzeybT3F6KwAtHs278OijPldAKbq5YLmdtdgfPCxzP87wgY+keYX5/GBL9wh+Tpa2vtdzNWoVCIWkdjTp4oSSrqKa81nb4UumoPi08YXzzjkFOmZGIpAW70o0tJL/CHBOiLtCbu4eo27Ici51XyRlCiEi14S/6lg3sGeZHWtpjcG+ZhJAqkz3Qr1XdiObWUsds59Vyxmzn1QpG0hURQgr15uhgdZveq7YV0MCxzPJxyeGhWkXvbrqUvhGeWv/1SvNj/++ns3lVCqVSUAbKY8JppabuVQ+9y9AID31U2olP/SW4+aGxEUIIYX4vnq+t0Dv0/7c7653vLt91e/d3Hh0mVX2tRbnZW01JSSGEHDx4UOJ6yM9X9Gu/zTh5VV9rcTA0xbIMRTF2h4MwMqa53WjXu+GEkLbsiRNCREGgaNr9Pfqm6hE9stN48yIEXqAZmrjscLR0pTwvML+/6a3pRjSnweJEkVBU21bUkh15mqHr3jRCqDbu+bSRze6Qy1ie5zmOF0TRTyUbFh/8l+lJt/cKbvUyV2y/8O73mQFaP3NAT1op5S4FgM9wVBWRqgKdkvny+XHJ0f6SrkupVH7xxRczZ850p7EPna/NKq55Yv3xrOIapVrDayLVUZGE+a08pXcr6wiYW/7Tdu08cI6q96/UmHr/+gLnUSPW+YHkOVt18U85+oNvHuwdqfvgf0b0jnT3PnB1nv/Pyc+P5apCY6yB3TEAEroOWWA3EthNLL805X/3b/7LmNGJYd6u6De+8jFcvSsj5bW9FVaiih9Gdx8oC+5eF7QAXQvDyoK7090HquKHVVhJymt7V+/KaNEC3vnu8ub0XFlEAgnsLlGNAL7MGpocEBT0wDvpnCB4u5bf+ETWvrn9wls7LzJB0dawfhTbsc50AEiFYuXWsH5MUPRbOy++uf2Cm3MJAlm185J/eDSr85U9eoD2ZwpMpGXKycsOeLuQ33g/a49nla/79rIsJEYW4u6NJQG6DudHY923l49nuXXvtvnrjqhUCkdArNSFAfg4ISI5o7D6m1PXvV0IIb6QtQs/+iUoOEgWjINdAI2TBXcPCg5a+NEv7jQ+mlFm02G3FYBQrFzuF/D2rkveLoQQr2ft5+m5xdUWS0iSd8sA8HGWkKTiasvn6bmum7214yLNMIzWQ3dRAOjgqKCYzCKDt6sgxOtZu3Fvtlwj7bBsgM5BrvHfuDfbdZvdZ4vlmhYPWgborGiVlmWYD/df8XYh3s7arOIa0T/SuzUAdAiif2RWcY3rNrllRk4Z2D71AHQIKrX68KVSb1fh1aw9nlUuiITRBHmxBoCOgtEECSJxPULKbOMYP3ygAG6gFeqrpUZvV+HVrD2aWa6U4yJaAHcp5bKjmU1mbU6pkRCCq+YA6rNQyiqjzdtVeDVrc0qNrAzfCwDuYmWynKb30Av0ZkrqJ7EAdDQio7A4eG9X4dWsray1CZTv3CYPwNcJFFNZ2+Qe+tXiGi8+ngXAR4k8x3v/IXXePISbV2602gUvPD8IoGOy2h155U3uoUcFt/DxUwBdAMXIfWEP1Jv92m6BasLIvFgAQAfDyLsFIlABOh7v3zcKANzkC7vnANAKyFqAjgNhC9AxeTVr8cUBAABdAPq1AB0G9k4BOihkLUAncb3S7O0SAHyO6PD+jSwIshagI3HZsY0KwhBlgIYomVQXljIMk5+f72Zjb2ZtmcEqcg4vFgCtwJurij+8t2rfGjfbG3/dWvzhvby5StKqugiRc1Q0fS8L8Ajjr1sL16Q6yq96uxDwdTzPx8S4+6xob2ZtmE5Jsbi+FsBdNCsL0eLuLwAdj7tZ63A4HA4P90G9f9csgA6FwugoHyPyjtqfPzMc/agd1iXYTNUH3jGd/7Yd1gXuEEVRr9e72djdrJXJZDKZh/ugFL43AFoGO6g+RuBtBWcFazPPFfYIkbdbco77yEgfIIRQFBUcHOxmY4yNAugkkMMAPsurj4/Fd4OHiA6rfudrjH+EJnlS1f53LJkHGV24/7gntUPvdpRlO6fQ6gD/cU/qht9PyZQ3ZrSba09tMZ7Zac35iVYHqHqN9R/9sLLHiBvHHETRXpxhOPqh6eIPhBDNbVP8Bs++ZfWi9dpxw5EPLNlHCcNq+k0PnLiIDezeThvflfB2W6Gea+rVihr0eFrM+RGoPbnFlv+rImawdug8ir7p4WOCubr2l89Nl/Zac36iFX6qxJTAiYvk3W4jhBh/3Vq2eaGzWdXutwgh3Z7ZoYwb5mIWQogocOaLe2pPpFlzfyEMq0meFDT1RUYX7nzVXpxhOPKB6eIPhOdUiSkBdyxUdOtLKKpq3xrnKiyZP5ZtXijvlhx+/wZZaHy7vVEdlygK3i6BEPeztqSkRNI6oO1M578lAh86ZyWl0BhPb9Nve9l+/TxvqgyavlR2//u2wrMV218hhPiPfswZpXxtecX2VyiGCZ23WhYUI3I2c8a+8i0vBE56wW/gXc421mvHy7/FXHEBAAAgAElEQVRaHJDyTMisf1Gs3Hb9fNXuVbbck8q4Yb+tVRSNZ7YbDq0Pnrks/MEPRc5ec+LT0rQFYfNWy8ISvPdmdE6sQhEd3OQjn0N1GDbVMqLdrP/2n3xNadj8d2XBcYLNWHvi0+r0f9MqXV0bR2U+YeXh969n/EJFh6X6x/XlXy8Jm/+uLKSH3+DZmn7TnLu5ganPuTMLEcWaY/8xX9wdPHOZPDxRdFjMWYcEh9UZ79acnyp2LA28Y1HInJWEEPP578q/fC5k5jJlz5GBqc/pRj1c9tkz2sFzGtnfhaZRFC36wPlKd48hR0REREREeHbdFgcvCj6xx9E5MOpA7fD7aZU/RbOavlPVfaeYLu0NSHlGFhxLKEoRPUCdmGK99rNgqyWEEIGv/eVz0WEJ/sM/nA0omVLTb3rAhAWGox9xVQWEEN5YUX14g3boPO2QuZRMSShaET0gcNLzlEJTt1JHRY7h8IagaS8rewwnFE3JlNqh82TBscZz3xIRBy48jLfbCnHDCs8xXfjBmvtz0LSXZCE9CEXRSq1u9GOa2ybXb6OI7u8/+lFGG0YoipKr/QbdJXJWa95JF4t1MYtgrbFkHVbGj5JH9HG+quk7VRYcS37/uPmPekTT/w8UzVI0q+43Td17gvH0dtFhlfR96NxEh80Hotar52tZmqIwPspzZGEJjCbQ+X9a4ScL6SGPTGKD4357maJkIT34mhLBUkMI4Y0VlqxD6uQ7GW3YjUVQlLLnCJGzWgvOEEIcZVccpdnqpFRS76gaGxwnj0yq+9Fy9Rit8pdH9a2bQit1stB4R0WOYDdJt7FdEytXRuOGFR4iOiyWnGPqPhNldZ8RQihWrug+yMVctCaI0YZxVYXur6j+LBQjYzTB9sJznKHhkUJ70SWuskDZY3jdGRyKZuWRyY7yq4LF4P7qoAFKpvCFvX5vnq+VsTTGInsQow2l2GaOIgp2s8jZCCGCzchbqmVBsQ0a/Pa9oM8jhHDVRax/JKMJcrFA3lhhyT6S949+DaarEieIPG5U4mn4uHiO4LBy1UWK6AHk5hO0DYkiZyi2ZB1ylF+1FV3kqgod5VdvnENp4SyUXK0bcX/FjqWFq+/QDpmjvf1eeXhvQtGEEN5YYS/OKHhrXIOFybslCzYjQ8LbuL3gXV4dGwXeRrFNnvz7Dc0Qppk/Er8hc0Jm/YtW+HmsLGiC66i1OPh2qqMTqT9U8FaiwBkObzSe3OI3ZI6m71T/Cf9DeK7sy+faMosidki3/9lqvnyg9pcvCt++w3/s40FT/k7J1YQQVfyosHvfqxsnBZ0JxiF3UbTCj1EF2MuylT1H1J8umCr52jJ5RCIhhFb68cYKwVTFaOpdQ8bZCWe/sRy5hq8uEm0mgqxtBy5PgatkLvtncDOKZmiFH6fPJ6JY/wBb/XMfnD7PeHpr4OS/afpNc05p9m6j7sxCyZSaftM0faeYM/aVf71EET3Qb/BsWunHGYp5UyWy1rNEzuELWYPra7soxi9E2WO4+dJevrbsxlRRtBWcoeUa5xlZWWg8oSjrtRP1v+KtBWdshefqflT2GGYvv2q5kt6OtXdhFBEw4sxDaIVWHplsvXacqymtmyhYDJbsozd+NFdz+nxGG1o3hdPn2oszGixKsBjqzpi4OQshhFC0Im6YPDKJN1YQQuQRfWh1oDljHxFcHZ9wNgb3UazMF06+eDNr9UYbnj3gNTSjG/UwRTP6Xf9w6POIKIoOq+n8t9WH1gdN/bvz6lhZcJzfoNlV+9eZM/aJAicKnCXrUE36x7Q6oG4x8qh+/mMer9q3xnxpr8g7iCjy5qqaY/+pn8fgKRSh6KaHOFyvNGOwYQtQlN/AmYLdUrX3bb6mlBDC15RWH3iXMxTVNWEDoxUxg4y/bhUsBiIKtsKz1Yc2UMyNO+hRrEIW2tNedLFun9X1LILFYDj6IVddRERBFDhL1mHBVKlKGEsIYQO7+495zJD+ce2pLaLdTAgRrDWmc7vMl/Y456XlGnlovK3ognN4I3Qs3jyGrFawhMY1P17DaEND71lX+/Pmsk+esF2/wAZGa/pNu+kCeZrxH/ekLDjOcPTDkn8/JAuN1w67O2TW8oodr9QthKJZ/7FPyEJ+a0Mr/BQxgzS3TZGFxDW+VmgDnnNUmZvs10YFqUX0eltCFtIj/IGNhiMbr//fHwRztSphjP/YJ+SRSdWH1jsbMLrw4BmvVu1/J/9/h9HqAE3/PwSm/kX/zRs3FkFRfkPm2MuuFK5KIQwb+XiaovsgV7PQrGCqLN54t6P8KhsYrU6cEHbve7994ijKb+BMRhdWc/Qj/Y5XBZtRETtE3Wei7vb5v62KletGP1r57T9zX+2jiB3ivCa4Hd8taBPKzQ9nSkoKIeTgwYMeXPefVh06UWBTdG84hBUAGkWXXOwfLvv6b+MbfXX3maJH1x9XJoxs56oAfBlvrBRLM/PWe/4GIEql8osvvpg5c6Y7jXG+FqCTqDLbm28EAN7g1azF4S4AzwlUN3cFFwB4Cfq1AB0GBj4BdFDIWoCOA2EL0DF5NWspwtL48gBwm4i4BeiQvJm1agUro3DND4C7BIFXK5q8Tu+Xq3pc8wPQgGA1cbz3PxfezNpwfyURmnzwNQA0wDkc4f5N3r93REIojhMBNEArVDLW+2dLvVlBnyh/ux33jQJwF885+kT5N/VqmL9C8P7uO4BvEXmHvItn7eSB3ewO9GsB3MVx/OSB3Zp6NTnaH8eQARoQbRY/paz5dhLzZtZ2D1ar5Kyj8roXawDoKByV11Vytntwk8+KZxlaxtK8sbI9qwLwdQ5zTEiTn5p24+We9ajEELm5rPl2AF2e3Fw2KjHEdZuIABVtQdYC3EA5LEN7BjffTmJeztrX7x5gMpkFs8G7ZQD4OMFsMJnMr989wHWzEQkh+DQB1CPY7fZ7x/bwdhneztr4cO3oPmGs/op3ywDwcaz+yug+YfHhWtfNnpmcaLNaCYcbIwMQQohQfi3IT9Ez3E+KhbMsm5+f72Zj74/O+uqv4xhKEK5f8HYhAD5KuH6BoYSv/jqu2ZaJUbrYUD9HaXY7VAXg+8TaintGS/XkQY7jYmJi3Gzs/awlhHyzJIXYTXzhWW8XAuBz2OJzxG76ZkmKm+1fm9vfYawWLDiSDF2duvqKUkYtneMTj231iaztFak9/da0UKVozf5JKM/xdjkAPoGtvOa48lOAXDj91rRekc0cPa4zdVC38cnhVGmmpLUB+DiuurSitPSf8wd6u5DfuJu1eXl5ubm50tURoJb/smLqoqm9lTa9JesYW3SGL7vK1ZQLdot0KwXwKYLdwtWUUxVXxYLTlqxjtLnimSm9TyyfGtDCh+V9/tzYyAAlXXCSCLxEpQL4Mq662FF25YFxPeeMiJVuLS06X0u5efF7SkoKIeTgwYOtr8tt+8+XbD567VxedWmN1cEJuDwfugiKohQyOlSn7B8TMH9Mj4n9ItqytNQ39mUW1VCB0bLg7p6qEMD3aSozy8srHrmj178k7tQqlcovvvhi5syZ7jRu8j7mXjSxX0SDbxmrnVfKGW/VAyA1Kf7C972auvTzM//5MUc0lij9Q226GEL7xDkjACkIllrKUGivrdZqFZ/8eUxq/zbtqnqcu1lbVlbmxf4lghY6N4n+wpfdM3DZPQNf2HTq+9NF5tLrchlLsQqOlssVCoq5eY1io/9t8ENDYhP/b5TgsNKsotmWjb/qch7BbqHlKue/zVXRYqLAUzRDnPXLmnzwQ4sXy9kpVi7wHM14rMPz+zvgsScvijwnigLNtuwURlOcJwQ9+ztS0jwr2jmb1WK18YLYI0zz2PQBj6bEe3AVnuLurzksLEzSOgBAIqseHLLqwSGlBuvXx/N/zdEXVpoNZqPd0d6ncm2ioBDNnlpa/fC1UYKCWG0UryBWTy2/DieKLKEIIVaRV5Ibw0fc7Xk00c4uCnKR5ojIim3Nxbo12AivEK0efMgxR0RBFOWiq8MhDbev6ffFTnhCKLngmd+Rn0pms3MqGROkVcSHh49ODPvTCHcvv/EKXzyGDAAeF+6vXDC5t7erAOiicP4GAABAWshaAAAAaSFrAQAApIWsBQAAkBayFgAAQFrIWgAAAGkhawEAAKSFrAUAAJAWshYAAEBayFoAAABpIWsBAACk5ev3Q9Yb7cczy3PKaqtMdkHonA+yzS03xYVqvF1Fa+SUGnuG+7X0pY4rt8wYF+a7G1VUaekW5NZDVK6VGXv48Ia0iIMTZDLv9Rma+E6ycoKSdVVVx/2AlNVYw3Qee96Rx53Lqx7aKzhMp+gT5T+mjw89MsdHs/Y/B69uTs/NLKqxOXiWoRmGoWia0KzdwTmf7Me2+RFkHnoShgcIgkDTem9X0TjX75IgCIczK1v6Urvy6K9Z4AW6sY3ykb8lQRBo955Qy/Pe+e1I8UZ57qk2hBDPLau5sgReOJpd9yugfp+HEEL5yJ9TU9z/M5Nao2+Ug+PO5BusDp4IgiiKgRr50PjgZ6f3GdIzuL3ru5nPZe2731/+vx8yTTaeKLRMcE+1/007Jr67N9Uludjh6ZQPHPbxjXL/+88nvik9xMeTqSk+/rfkgu/88TR6TMEZac7DOwJnt9eW/HytcsbygwmRuv/3xO19uwe0X32NFeYrpv/rwJm8qsDQCEX3nt6uBQAAOjCalYuBMY7AGBVnzym6PGnZvqfu7P3a3P5eKcaHsnbESz+U1jjkPYZYWIW3awEAgE6CYuWKmP6Oyusb9mbVWhyrHhzS/jX4yvGAWW8dKq2x03FDaQQtAAB4miwoShbeK+3Itf/8eLX91+4TWbs5PfdEdjmJTPZ2IQAA0Gmx/uFB4ZH//Op8+6/aJ7J25faLQSGhtKJDXvcCAAAdhTWgp0CoFzadauf1ej9rj2aUlRoslqDe3i4EAAA6P84vfPsvhR5ZVH5+vpstvZ+1Hx64IleqvV0FAAB0CbKQWLPNcSK7ou2LiomJcbOl97P29LVKUeW1a54AAKCrUSmVXx93t0vqEd7PWn2tjfYL8XYVAADQVchV6nN5Ve25Ri9nbaXRzgsirdJ6twwAAOg6HKympNrSnmv0ctZeKamhff32nwAA0Kk4KIXRyrV9OQaDwc2W3u/XUshaAABoRxQrc/BC25fj7+/vZkt379FotVpbW4wrFCE04/1zxgAA0KWwTLs+AMLdrFUqpXrEDs8LMokWDQAA0BgHx7fn6tCnBAAAkJa7/VpB8MChbQAAgC7I3aylafSAAQCgMxAdNrHRZ81LBgkKAABdCyVTtPMVMMhaAADoWkSHrX27tchaAADoYiiZop1v7ICsBfAOwWIo27xQv/M10eGZi9dFh7Xi6yVV+9Y4f+SqCos33m04vIEI7XptAwDcClkL4B2iwBHeA3eJa3L5PCdydumWDwDuc3ccMgB4FqMJDrv/femWLwuJ67Zgm3TLB+i4RIeNotv1KLKX+7XXK83eLQAAALoaSqYQhXYdHeXlrA3WSnXrRwAAgEaJDhvdvv1aLx9DVspwwhg6J+OvW2t//Sp07tvmS3trT35py/9VETNYO3Sedug8SqYkhIgOq37na4x/RGDqc4QQ3lxV9tkz2sFz5JFJhiMfmC7+QAjR3DbFf+wT8sikusWKDmvtyS+dC5SFxutGPaS7/V5Krr61gLoF+g2eXe/HP8nCe1cf+D9L5kFaHaAderf/+KdohV/dXPbijN/WznOqxJSAOxYquvUleBgXdC6UTCG0b78W52sBpMLXlul3/kNz26RuT39FsXJb/umKna8J1lr/8U9RdOMfPdOlPZbsIwGpfwmd+7ZgMxoObyz/6m9hd6+RhSUQQkS7Wf/tPwWbMfzBD1j/SM5QXPn9cn15TvD0VxqN28aWv1dWlh0y+1+MOtBZDxGFwDufJzRDCLHm/FSxY2ngHYtC5qwkhJjPf1f+5XMhM5cpe4703LsC0BWhWwkgFXvRJXXvcX6DZlMyJaFoReyQgDv+XPPTf+1Fl5qaRTDpA6cskQXHEoqilVr/sY+zAVE1J9JE3kEIMV34wVGWHTT1JdY/khDC+kf6j33SciXdVnTR3Zp4R8D4/2E0wYSiFTGD/Qb8wXLlCGcoJoTwxorqwxv8Rz2i6f8HimYpmlX3m6buPcF4erunrkoC8CG4bxRA5yCPTFL2GF7/AKyi222sf4S9NLOpWZQ9RrC68LofaZW/MnaIvThDsNYINqM568cGDdjAKDYwyl5y2c2SFN0H0qrfn25NUYroAYLN5IxSe9ElrrKgfsEUzcojkx3lVwWLwd1tBugIRIeNad8zI14+hny90owzQdBZMX4htCaw/hRKoaEUflxVYVOzsMExDU6OMn4hvEkvmKtpTRBvrDCe+qpq79sN5lLGDXOzJDYg6qafacZenMGbq2SE8MYKe3FGwVvjGswi75Ys2IwMCScAnQUlU/Dt+/ABL2dtVJC6nR+2ANB+aIZQjRw6ouWapuagKKax9mqKVTj/H/KnN3UjHvBUgQ2o4keF3fseo0OsAngYjiEDSEUwVQrmm46+CkY9X1MqC4lrahau+vpNP4uio+Iao4ugVTqKZmhnn1iaey7SSj/OUMybKqVYOIBPER32tnfzGIbJz893szGyFkAqtsJz9uJ6w6BE0Zz1I63wq38NT8NZCs7UPznKVRdasg8re9xOK7S0QquMHWK+vN+hz5WiWnlEH1odaM7Yh/snQ6dHyeRtP33J83xMTIybjZG1AFJhA6NrfvqvJeuQKHCiw2o8vbUm/d+6UQ+zAdFNzcLVllUfeJevKSWi6NDnVX6/nNEE+Q2YSSiKUJTfoNmywO6V3/3LUX6ViIIocI7ynOoD73pk7BIb2N1/zGOG9I9rT20R7WZCiGCtMZ3bZb60p+0LB+jicH0tgFRkYb2CZ7xae3JL+Vd/E8zVqoQxoXNXKXuMcHFrCP+RD9Eq/7LPF1myj8hC47XD7tYNv59WBzhfZbShIXNX1f68ueTfDzvKr7KB0YqYwbph91CKJk8AtwBF+Q2cyejCao5+pN/xqmAzKmKHqPtM1N0+3wMLB+jafGEcMgYiQ6fF6MKDZywNnrH01pcomTLkT2/eMpVWJ9+pTr6zyQVqggNSFgakLGx2gYw6MPLxz5r60UkZN6znW0X1166KH62KH+1iiwCgjsVicbOll48hRwWpRQxEBgCA9uWRTp5KpXKzJc7XAgAASAtZCwAAIC1kLQAAgLS8PDbql6t6nK+FTslv8Gznw+zc1OjYJQCQgmA18XxXelb8iIRQjEMGAID2RCtUMrZd48/LWRsRoEC/FgAA2pPIOeRdKmv7xgSKokgEwbtlAABA1yHYzVqVrD3X6P2xUQoZg9udAwBAu6Ed5h6hnrjbmtvcHRtVUlIiUQVRQaoicyXRhki0fAAAgPpEu3lEYpO3JZeCu/1am81ms9mkqGD8bRG82QN3TgcAAGiW4LA6HNzjE3u150rdzdrY2NjY2FgpKnhpVl+H3S4gbgEAQHry6ryYEE2AWt6eK/X++Vo/JTukZ5DSIMkjOQEAAOoRjNX6BZMT23mt3s9aQsjHz4yqNZpIVYG3CwEAgM5MXnKhR6jfQxN6tvN6fSJrw3TKP0/tYynP52rKvF0LAAB0TrbrGRazeeffU9p/1T6RtYSQJXfddveoOHtxNluNg8kAAOBh8tILtKV6059HB/m165laJy/fD7m+NQ8P7Rnut2rnJYVRb9NGs/7h3q4IAAA6PLbymqWqNFCn+GHZ5Nj2vaz2Rg1eWWtT/jy1z5+n9nnw3fSDF3Os5ddkSrWg0NFqHaXQ0KzC29UBAIDPEwSBswqWWgVXa6oxiA6bv1q2YHLC3+/q68WifCtrnTb9eTQh5N8Hr3736/WM6/qa4mJOEESRxIRq88trvV0dAAD4NJqilHIm2F95x4DQ+8b2GJsU5u2KfDJrnR5JiX8kJb7uR8F3zi0DAIBPstoFpdwXs8IXa2pUhykUAAC8xDeDliDCAAAAWoFhmPz8fDcbI2sBAABajOf5mJgYNxsjawEAAKSFrAUAAGgxURRxDBkAAEBCFEXhGDIAAICvQNYCAABIC1kLAAAgLWQtAACAtJC1AAAA0kLWAgAASAtZCwAAIC1kLQAAgLSQtQAAANJC1gIAAEgLWQsAACAtZC0AAIC0kLUAAADSQtYCAABIC1kLAAAgLWQtAACAtFj3m/74448URUlXCgAAQEehUCjcb0yJoihdKQAAAIBjyAAAANJC1gIAAEgLWQsAACAtZC0AAIC0kLUAAADSQtYCAABIC1kLAAAgLWQtAACAtJC1AAAA0kLWAgAASAtZCwAAIC1kLQAAgLSQtQAAANJC1gIAAEgLWQsAACAtZC0AAIC0kLUAAADSQtYCAABIC1kLAAAgLWQtAACAtJC1AAAA0kLWAgAASAtZCwAAIC1kLQAAgLSQtQAAANJC1gIAAEgLWQsAACAttnWzVX39f8YT3zsKs3ljtcg5iCjKQ0Pt5eWeLQ4AAMBb5N1i7UV5hKIohmU0OllMH7/hUwP/tLAVi6JEUXS/NVdVVrziUcuFYzRNyf1VKrWgVNkUapqVt2LVAAAAvo6zE5tZsNaKFjNrN/GCIKr6jor8+8dsYJj7C2lB1pa+94Lh24/l/trgEKMmCAefAQCgyzFVCvoKP7uh1n/6o+HPrHJzLneztvDvf7RcSA+OlQWE8G0oEgAAoMOrrmD0eQ5V39HRK3a6096trC16/V7TyT3d+1BydZsLBAAA6PjsZlJwWdQMndTttc+abdz8oWDDD5uMx7+LjBcRtAAAAE5yNYmMF43HvzP8sKnZxs33a6/eHa/2p8IjDR4qDwAAoJMoLfY3G8T4L666btZMv7Z650bBZEDQAgAA3Co80iCYDNU7N7pu1kzWGr75UKXzXFEAAACdi0pHDN986LpNM1lrL7rqH9qCC3ABAAC6FP9Q0V7UhmPIlovHRUHQBOBSWgAAgMZpAmhRECwXj7to4ypHzeeOMAqlp6sCAADoVBiF0nzuiIsGrrLWUXiFVrTyhskAAABdBK1gHYVXXDVw8RpfU8EwOFkLAADgCsOIfE2Fiwaus7aKOGyeLgkAAKBTER02vqbKRQNXWUsp1RThPF0SAABAp0JTPKVwdW9Fl1nr6WoAAAC6IFdZ25In2wIAAHRZzXROXfZr0bEFAABoVnNx6eqSHnRrAQAAmiUKhDjsLhrgfC0AAECbUDRFyeQuGuBWFQAAAG3C2UVSWeKigctjyDiIDAAA0BxWQZOgCBcN8FwBAM/7Jss+8P3q9AKHRO0BwLc01zXFMWQA8tl52wPbaht9aXK8/JPZ2mBVy0YvWLmWHRRqafvW+edh87ECrhWbAwDNaMs4ZK7iOrHjODJ0fvf2U9zbT+H8/2fnbZ+es7UxkOYkK+YkK6RrDwA+pg3X17KhUawc+78AAABtgvO1AAAAbdSGfi0RcY0tAPnsvG1aWk2Wnn/lgDn0Lf0/D5sJIZUWcfVPljv+a2DeqAhYob/7q9ozJTce1JFe4Bi8oTpTz9dfQr5BeOeEJfm9KuaNikmfGI4VOMQm2jt/PF/GfX7BNuqjauaNilEfVW/LsAv1TukIIvkx1zH7y5qAFfqe6yr/use0L8cxeEP1Z+db+WwuGy9uzbBN/6wmYIU+YIV++mc12zLsnHBTm3Kz8Gb6jU3YlmF/9aB5WlqN3vJbZQU1wgt7TM4GQzdWp7W2GICOhedFwWRw0aC5sVE4XQtASLVVWHvc8sgg5bI71M79z2vVvJwhn/1JG+5Hmx3iqmOWBd8a/ztLmxDENLqEEqOw9KDpgf7KCwsCjXZxzXHLk7uMX8zV3RbaeHuzQ1yZbpnSS37wYX9BJGnnbAu+M/ortXf0kBFCREK+uGhbfsS8IlXz5RwdIWTPVfsLe0y51XzrNtDkEF85YL5cwS1L0QyOZAWRpOc7Xtxvyq7knx+pYmlCCCk1CU9/Y4zS0vsf8o/0o4uNwr+OmL+6ZB8U8dvXSG41/+A24+wk+amnAhQMdbWKP1WEB4VBl8CwNNH4u2jg8tkDPIdLbAEIISeuc3f2lA/rxtYd6BkSyS68XRXhR1OEaGTUPX0VVo4cL2wyWs6WcnclKlJ7ymiK6BTUgwMUOgW192qTN3XLruQHR7L39lMoGErFUn9KVozqzn6bbbfzhBCSpeffSrf8fYx6WoKcpQlLk2kJ8tfGq2tbO5hxx2X7oVzH6sl+Q7uxNEVYmoyPk70+QfP+ScuvxRwhhBfJv0/bREJeHa+O9KMJIZF+9BspmlHdb+yv/3ydM9rFqQlyFUvRFEkIYu7piwFfAIQ0dwwZSQtACCH9wtikJjqgTiFqOsKPzmu6Wzkkku0XfmMJ4Rq6bxhrsDX5GdPKqZHRsrpoD1BS/cPZWpvICSIh5FQRF6iiJsTJ6s+SHMYMCG/NVXy1dnH3VfvUBFnv4Ju2cVAkkxTKHrjmIIRUWYQj+Y5pveRhmhtfGoFKqq5TSwgJUdPFRuFkESfgmwPgZq4+mbRMJuJ8LQAh4X5UsPqmD4NISGGNsPeqPVPPnyvl8qqFTD1fv5PXQIiaDlTdSCmKIgxNSoyClRNVbCMfs56BTGC9i44oQhiKFNYIFo6oZSSnio/W0VqFZz6fdp6Um8TR3Rnm5uVp5FSMP11Yw1s5UW8Ri2uFnoGudjiGR7NPDFY+863xg1PWp4YqpyfIdR6qEMDHiSIhnKt70WAcMkDzGIqi6z1jkhPI28cs09MMFWZxVh/Fplna/Q/5T453dedxj1PLKNajH1+VrPlcVMlcvaqRUa+nqH99KmBsrOzl/aYRH1YfycOdsKBLoGiKYl19PJC1AC12tYr/7Lzt9QmaxaNVI6LZUDUtZ9q1A8fSVE4Vb7z57KyNE82O1hy9lTMkVENdruD4m+c22cV8g9AriFGyFEMRliY5VTeNS+ZFYrn5jlcUIfgI9EQAAAXFSURBVD0DmWUp6hNPBIyIlq1It1SYbx7KDNAlIWsBWqzKIuZU8WF+N/L1aiV/rrT9xtzeHsWeK+XPFN84PSwS8mOuI7uyNeOQtXJqfKzs+2xHlv6m2c+X8gUGfkyMjBASqaX7hrH7c+w1thvhWmDgj+Q1vtWhajq1p6zGJprQs4WuQCQi39pjyBgaBdCo2AB6eDT72XlblVUURHKyiFt93CJ3dSrTw0Z2Z+cmy1/70XS8kBNEYuHEz87btmbYnSOEW+HuvopR3dnndxudI5s4gRzKdbxy0PTiWPWQbiwhRCOjHhusOJznWHPcUmMTRUJyqvg3Dlnq9613Zdr3XnU4e7qFNcI3WfbxsbIoLXbooQugaYpxdQwZzx4AaLFIP/rNVM3yo5YeayuDVNScZMUrY9WL95rarQAVS715p2bTWdvzu40nrnPDo9iHBirXTtE8usPoesbdV+1hb+nrT3l9gvqVcWqNjHprkmbLRfvivaZDeY4gFfXHRMXqyX63R90YuDW6u2zHfN3a49b4dyoJIX9MVDw3Urnjsv1YwW9d20AVtfyoZe4WR61dHB8re3igcu5tcs+eVAbooCix6d5r3jNjxJILMX1dDocAAN+Qqefnf1X79zGqebe131Wt/zxsPnGd2zRLG6jEkGPouq5flZNug7qv/KapBi6v+dEEkPYd8QEArVZhFqqtQnwTN66SgoUTr9cKvYMZjRtjmAE6tbbcDxkAOghOIN9nO4ZFyVxfAutZ2Xr+pwJufKysPc9VA/ii5vY2XWYtdlUBfFKVVVxx1HK+jOMEIhJSYhT+edj8wxX7C6NU0h3L3ZVp33zBVmUVCSF2nvyY61j4nWlGb/md8TjNBNDM5w5jowA6HiVL/OTkr7tNP1/nau3ioAh2VpJ86926GH8Jj1TFBNDrjluXHTJn6vkgFXVHD/nLY1UTesgUONME0BxkLUDHo2KphberFt6uas+VDghnP57p155rBOg0XO0FUxRF2Ha97RwAAEBHRNGuDvC4ylpa5ScSZC0AAIArAi/QKldHfVxlLRMUwXO4dxQAAIArgo1jgiJcNHCVtYoet/G2Jp9lDQAAAIQQ3sEretzmooGrrPUbPkWwI2sBAABcERyc3/ApLhq4ylo2NJpWqqpL8EgsAACAxlWXCLRCxYZGu2jTzNV4yqQRNdXtd29VAACAjqWmWqFMHuG6TTNZG/LgS/Yas93quaIAAAA6C7uV2GvMIQ++5LpZc/3aPsOU8f1Lr+MCdgAAgIZKr/sp4/sr+wxz3czVM/Wc+Bp9zr2JujA6tDuu/wEAAPhNeQFVUyb0/CyT0QW7btn83VMZXXDo028aiu0V+byHygMAAOjYKvJ5Q7E99Ok3mw1a4ub9kANmPEZ4e9nGV2y8NqpHbZsrBAAA6MAKr2mtekPY08sDZjzmTvvmjyHXsRdkXv/HfEdxripIFRRiVUn5RBEAAABfYzEIlRVKS6VFFhkX9dpmeUyimzO2IGudag58WbVlnS0vg6IJq2BZlmPlhKnrHjMu7p8sweneRhbZ9rU0t4TfX3dYBZnytx0OgRfpFjxZrNkipTk1Ljb5Q6MEntD1ngHOO0RG1tmenuawirKbH/h665SOqJGtEDvDE6kdNlGmuLEZHf2XJYqE+r38jr4tTs6t6BzbUoenVDwvc1g4zmwRRVERkxQ491ndHfNatJAWZ22d2sPbzKd/tBdm84YKwWZh1FreaGjdojoo0W6h5L8/1KxBLnUGIuF5wtzYKJFzUGxneyq46LBRspuuIBdtVkqhbNEyPFqQhxZzy3b5QNh6YNsabFdjm9mh1Atb0WGnZHV9lSbeK58fnypydoqV37wtkq+zsYmUR94sRq3lbVZaqWH8Q+TRCepBE7TjZrVuUa3PWgAAAHAHzrkCAABIC1kLAAAgLWQtAACAtJC1AAAA0kLWAgAASAtZCwAAIC1kLQAAgLSQtQAAANL6/7Xa13ykzXgRAAAAAElFTkSuQmCC"
-    }
-   },
    "cell_type": "markdown",
-   "id": "be8ece76",
+   "id": "96bbfc12",
    "metadata": {},
    "source": [
-    "### 🤔 The Need For Configuration Files And Its Structure\n",
-    "\n",
-    "In short, they make our lives easier, especially when dealing with something as iterative as machile learning projects and experiments. There are two important reasons for this:\n",
-    "\n",
-    "1. With config files, we can run our experiments through command line. We need not worry about internal implementations / optimisations in any way (atleast during training period). By doing this priority shifts from writing code to improving model accuracy, analyzing training curves and improving results in general.\n",
-    "\n",
-    "1. Machine learning projects have three essential components - a dataset, a model and a pipeline. Pipelines defines how a model interacts and learns from a dataset. Open3D-ML config files are designed on top of this idea, we can manipulate these components using flexible config files which let us plug and play different settings to them. Just a single look at config files (along with training logs) tells everything one needs to know about any machine learning expermiment\n",
+    "## Basic format\n",
+    "We use [YAML format](https://en.wikipedia.org/wiki/YAML) for defining our config files. A config file in Open3D-ML always has the following parameters:\n",
     "\n",
-    "![image.png](attachment:image.png)"
+    "1. `dataset`: Contains key-value pairs related to training, validation and test dataset.  \n",
+    "2. `model`: Contains key-value pairs realted to model architecture.\n",
+    "3. `pipeline`: Contains key-value pairs related to the training, testing and inference pipleine."
    ]
   },
   {
-   "attachments": {
-    "image-2.png": {
-     "image/png": 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"
-    }
-   },
    "cell_type": "markdown",
-   "id": "0427755b",
+   "id": "b79af95f",
    "metadata": {},
    "source": [
-    "### 📚📑 Dataset: Experience in It's Rawest Form. \n",
-    "\n",
-    "In all supervised machine learning algorithms, models learn by \"seeing\" hundereds, thousands or even millions of examples. A dataset is fundamentally a collection of these examples. In our case, a dataset is a collections of annotated point clouds.\n",
-    "\n",
-    "![image-2.png](attachment:image-2.png)\n"
+    "For example, `randlanet_semantickitti.yml` config file uses RandLA-Net *model* architecture, SemanticKITTI *dataset* and semantic segmentation *pipeline*. Let us have a look at it's contents as a raw text file."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "acb2fb3f",
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "40d8af8c",
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### 🧠🤖 Model: The Actual Learner.\n",
-    "\n",
-    "Models *operate* on high dimensional data and (usually) reduce them to valuable insights; These operations heavily rely on optimized parameters called weights. Model output schema dependS on multiple factors.\n",
-    "\n",
-    "In our case, model operates on high dimensional point cloud data and reduce it into lower dimensional insights such as - what kind of point cloud it is (entire point cloud classification) or what is type of each point in the point cloud (segmenation i.e point-wise classification) or what are the extents of a specific object (regression & classification). For different usecases we different model architectures. For example, RandLA-Net is an architecture used for point cloud segmentation tasks."
+    "cfg_file = \"../../../ml3d/configs/randlanet_semantickitti.yml\"\n",
+    "%pycat {cfg_file}"
    ]
   },
   {
-   "attachments": {
-    "image-2.png": {
-     "image/png": 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"
-    }
-   },
    "cell_type": "markdown",
-   "id": "c8319586",
+   "id": "31a57041",
    "metadata": {},
    "source": [
-    "### 👨‍🔧🚿 Pipelines: The Learning Process\n",
-    "\n",
-    "Pipelines in Open3D-ML define how a model and a dataset interact with each other. It also includes steps for training and inference.\n",
-    "\n",
-    "The below image shows semantic segmenation using RandLA-Net \n",
-    "\n",
-    "![image-2.png](attachment:image-2.png)"
+    "<div class=\"alert alert-info\">\n",
+    "**Note:** You may find such config files available at <a>https://github.com/isl-org/Open3D-ML/tree/master/ml3d/configs</a>\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b79af95f",
+   "id": "5a75070d",
    "metadata": {},
    "source": [
-    "#### An Example\n",
+    "### Reading a config file\n",
     "\n",
-    "`randlanet_semantickitti.yml` is a config file used for semantic segmentation pipeline, using RandLA-Net model architecture and Semantic KITTI dataset. Let us have a look at it's contents like raw text file."
+    "In order to access the key-value pairs we must load the file into memory as `Config` class object. `Config` class object's usage is very much similar to standard Python dictionary `dict`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "40d8af8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pycat \"../../../ml3d/configs/randlanet_semantickitti.yml\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a75070d",
+   "execution_count": 2,
+   "id": "8c60d9c0",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
+      "[Open3D INFO] WebRTC GUI backend enabled.\n",
+      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
+     ]
+    }
+   ],
    "source": [
-    "#### Read and Load Into Memory\n",
-    "\n",
-    "To be able to utilize key-value pairs and parameters present in the config file, we must load the file into memory as `Config` class object. `Config` class object's usage is very much similar to standard Python dictionary `dict`.\n",
+    "from open3d.ml import utils\n",
     "\n",
-    "`utils.Config.load_from_file` takes path to config file as input and returns `Config` class object as shown below:"
+    "cfg = utils.Config.load_from_file(cfg_file)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8c60d9c0",
+   "cell_type": "markdown",
+   "id": "7fe103b0",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from open3d.ml import utils\n",
+    "`utils.Config.load_from_file` takes path to config file as input and returns `Config`. \n",
     "\n",
-    "cfg = utils.Config.load_from_file(cfg_file)"
+    "<div class=\"alert alert-info\">\n",
+    "**Note:**  To avoid `FileNotFoundError`, always make sure that the config file exists at the given location. The config file must be a valid YAML file!  </div>"
    ]
   },
   {
@@ -165,17 +100,221 @@
    "id": "00a48be1",
    "metadata": {},
    "source": [
-    "#### Probe, Examine & Access Parameters\n",
+    "## Accessing parameters\n",
     "\n",
     "Built-in function `vars` grabs all the properties of the object as dictionary"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "da50bb7a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'_cfg_dict': {'dataset': {'name': 'SemanticKITTI',\n",
+       "   'dataset_path': None,\n",
+       "   'cache_dir': './logs/cache',\n",
+       "   'class_weights': [55437630,\n",
+       "    320797,\n",
+       "    541736,\n",
+       "    2578735,\n",
+       "    3274484,\n",
+       "    552662,\n",
+       "    184064,\n",
+       "    78858,\n",
+       "    240942562,\n",
+       "    17294618,\n",
+       "    170599734,\n",
+       "    6369672,\n",
+       "    230413074,\n",
+       "    101130274,\n",
+       "    476491114,\n",
+       "    9833174,\n",
+       "    129609852,\n",
+       "    4506626,\n",
+       "    1168181],\n",
+       "   'test_result_folder': './test',\n",
+       "   'test_split': ['11',\n",
+       "    '12',\n",
+       "    '13',\n",
+       "    '14',\n",
+       "    '15',\n",
+       "    '16',\n",
+       "    '17',\n",
+       "    '18',\n",
+       "    '19',\n",
+       "    '20',\n",
+       "    '21'],\n",
+       "   'training_split': ['00',\n",
+       "    '01',\n",
+       "    '02',\n",
+       "    '03',\n",
+       "    '04',\n",
+       "    '05',\n",
+       "    '06',\n",
+       "    '07',\n",
+       "    '09',\n",
+       "    '10'],\n",
+       "   'all_split': ['00',\n",
+       "    '01',\n",
+       "    '02',\n",
+       "    '03',\n",
+       "    '04',\n",
+       "    '05',\n",
+       "    '06',\n",
+       "    '07',\n",
+       "    '09',\n",
+       "    '08',\n",
+       "    '10',\n",
+       "    '11',\n",
+       "    '12',\n",
+       "    '13',\n",
+       "    '14',\n",
+       "    '15',\n",
+       "    '16',\n",
+       "    '17',\n",
+       "    '18',\n",
+       "    '19',\n",
+       "    '20',\n",
+       "    '21'],\n",
+       "   'validation_split': ['08'],\n",
+       "   'use_cache': True,\n",
+       "   'sampler': {'name': 'SemSegRandomSampler'}},\n",
+       "  'model': {'name': 'RandLANet',\n",
+       "   'batcher': 'DefaultBatcher',\n",
+       "   'ckpt_path': None,\n",
+       "   'num_neighbors': 16,\n",
+       "   'num_layers': 4,\n",
+       "   'num_points': 45056,\n",
+       "   'num_classes': 19,\n",
+       "   'ignored_label_inds': [0],\n",
+       "   'sub_sampling_ratio': [4, 4, 4, 4],\n",
+       "   'in_channels': 3,\n",
+       "   'dim_features': 8,\n",
+       "   'dim_output': [16, 64, 128, 256],\n",
+       "   'grid_size': 0.06,\n",
+       "   'augment': {'recenter': {'dim': [0, 1]}}},\n",
+       "  'pipeline': {'name': 'SemanticSegmentation',\n",
+       "   'optimizer': {'lr': 0.001},\n",
+       "   'batch_size': 4,\n",
+       "   'main_log_dir': './logs',\n",
+       "   'max_epoch': 100,\n",
+       "   'save_ckpt_freq': 5,\n",
+       "   'scheduler_gamma': 0.9886,\n",
+       "   'test_batch_size': 1,\n",
+       "   'train_sum_dir': 'train_log',\n",
+       "   'val_batch_size': 2,\n",
+       "   'summary': {'record_for': [],\n",
+       "    'max_pts': None,\n",
+       "    'use_reference': False,\n",
+       "    'max_outputs': 1}}},\n",
+       " 'cfg_dict': {'dataset': {'name': 'SemanticKITTI',\n",
+       "   'dataset_path': None,\n",
+       "   'cache_dir': './logs/cache',\n",
+       "   'class_weights': [55437630,\n",
+       "    320797,\n",
+       "    541736,\n",
+       "    2578735,\n",
+       "    3274484,\n",
+       "    552662,\n",
+       "    184064,\n",
+       "    78858,\n",
+       "    240942562,\n",
+       "    17294618,\n",
+       "    170599734,\n",
+       "    6369672,\n",
+       "    230413074,\n",
+       "    101130274,\n",
+       "    476491114,\n",
+       "    9833174,\n",
+       "    129609852,\n",
+       "    4506626,\n",
+       "    1168181],\n",
+       "   'test_result_folder': './test',\n",
+       "   'test_split': ['11',\n",
+       "    '12',\n",
+       "    '13',\n",
+       "    '14',\n",
+       "    '15',\n",
+       "    '16',\n",
+       "    '17',\n",
+       "    '18',\n",
+       "    '19',\n",
+       "    '20',\n",
+       "    '21'],\n",
+       "   'training_split': ['00',\n",
+       "    '01',\n",
+       "    '02',\n",
+       "    '03',\n",
+       "    '04',\n",
+       "    '05',\n",
+       "    '06',\n",
+       "    '07',\n",
+       "    '09',\n",
+       "    '10'],\n",
+       "   'all_split': ['00',\n",
+       "    '01',\n",
+       "    '02',\n",
+       "    '03',\n",
+       "    '04',\n",
+       "    '05',\n",
+       "    '06',\n",
+       "    '07',\n",
+       "    '09',\n",
+       "    '08',\n",
+       "    '10',\n",
+       "    '11',\n",
+       "    '12',\n",
+       "    '13',\n",
+       "    '14',\n",
+       "    '15',\n",
+       "    '16',\n",
+       "    '17',\n",
+       "    '18',\n",
+       "    '19',\n",
+       "    '20',\n",
+       "    '21'],\n",
+       "   'validation_split': ['08'],\n",
+       "   'use_cache': True,\n",
+       "   'sampler': {'name': 'SemSegRandomSampler'}},\n",
+       "  'model': {'name': 'RandLANet',\n",
+       "   'batcher': 'DefaultBatcher',\n",
+       "   'ckpt_path': None,\n",
+       "   'num_neighbors': 16,\n",
+       "   'num_layers': 4,\n",
+       "   'num_points': 45056,\n",
+       "   'num_classes': 19,\n",
+       "   'ignored_label_inds': [0],\n",
+       "   'sub_sampling_ratio': [4, 4, 4, 4],\n",
+       "   'in_channels': 3,\n",
+       "   'dim_features': 8,\n",
+       "   'dim_output': [16, 64, 128, 256],\n",
+       "   'grid_size': 0.06,\n",
+       "   'augment': {'recenter': {'dim': [0, 1]}}},\n",
+       "  'pipeline': {'name': 'SemanticSegmentation',\n",
+       "   'optimizer': {'lr': 0.001},\n",
+       "   'batch_size': 4,\n",
+       "   'main_log_dir': './logs',\n",
+       "   'max_epoch': 100,\n",
+       "   'save_ckpt_freq': 5,\n",
+       "   'scheduler_gamma': 0.9886,\n",
+       "   'test_batch_size': 1,\n",
+       "   'train_sum_dir': 'train_log',\n",
+       "   'val_batch_size': 2,\n",
+       "   'summary': {'record_for': [],\n",
+       "    'max_pts': None,\n",
+       "    'use_reference': False,\n",
+       "    'max_outputs': 1}}}}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "vars(cfg)"
    ]
@@ -185,35 +324,133 @@
    "id": "7af1de18",
    "metadata": {},
    "source": [
-    "List all the keys in the top most level:"
+    "You can list all the keys in the top most level using:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "e003542b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['dataset', 'model', 'pipeline'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cfg.keys()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "0a4923a4",
+   "metadata": {},
+   "source": [
+    "Here, you can see the three essential components."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1aa31b4e",
    "metadata": {},
    "source": [
-    "To access any property, use either `cfg.{property_name}` or `cfg['{property_name}']` syntax.\n",
+    "To access any property, use either `cfg.{property_name}` or `cfg['{property_name}']` syntax as in object **dot notation**.\n",
     "\n",
-    "Access `dataset` parameters using: (same can be done for `model` and `pipeline`)"
+    "For example, `dataset` dictionary can be accessed using the following code (same can be done for `model` and `pipeline`)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "ef76e079",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'SemanticKITTI',\n",
+       " 'dataset_path': None,\n",
+       " 'cache_dir': './logs/cache',\n",
+       " 'class_weights': [55437630,\n",
+       "  320797,\n",
+       "  541736,\n",
+       "  2578735,\n",
+       "  3274484,\n",
+       "  552662,\n",
+       "  184064,\n",
+       "  78858,\n",
+       "  240942562,\n",
+       "  17294618,\n",
+       "  170599734,\n",
+       "  6369672,\n",
+       "  230413074,\n",
+       "  101130274,\n",
+       "  476491114,\n",
+       "  9833174,\n",
+       "  129609852,\n",
+       "  4506626,\n",
+       "  1168181],\n",
+       " 'test_result_folder': './test',\n",
+       " 'test_split': ['11',\n",
+       "  '12',\n",
+       "  '13',\n",
+       "  '14',\n",
+       "  '15',\n",
+       "  '16',\n",
+       "  '17',\n",
+       "  '18',\n",
+       "  '19',\n",
+       "  '20',\n",
+       "  '21'],\n",
+       " 'training_split': ['00',\n",
+       "  '01',\n",
+       "  '02',\n",
+       "  '03',\n",
+       "  '04',\n",
+       "  '05',\n",
+       "  '06',\n",
+       "  '07',\n",
+       "  '09',\n",
+       "  '10'],\n",
+       " 'all_split': ['00',\n",
+       "  '01',\n",
+       "  '02',\n",
+       "  '03',\n",
+       "  '04',\n",
+       "  '05',\n",
+       "  '06',\n",
+       "  '07',\n",
+       "  '09',\n",
+       "  '08',\n",
+       "  '10',\n",
+       "  '11',\n",
+       "  '12',\n",
+       "  '13',\n",
+       "  '14',\n",
+       "  '15',\n",
+       "  '16',\n",
+       "  '17',\n",
+       "  '18',\n",
+       "  '19',\n",
+       "  '20',\n",
+       "  '21'],\n",
+       " 'validation_split': ['08'],\n",
+       " 'use_cache': True,\n",
+       " 'sampler': {'name': 'SemSegRandomSampler'}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cfg.dataset"
    ]
@@ -228,10 +465,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "a0312122",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'SemanticKITTI',\n",
+       " 'dataset_path': None,\n",
+       " 'cache_dir': './logs/cache',\n",
+       " 'class_weights': [55437630,\n",
+       "  320797,\n",
+       "  541736,\n",
+       "  2578735,\n",
+       "  3274484,\n",
+       "  552662,\n",
+       "  184064,\n",
+       "  78858,\n",
+       "  240942562,\n",
+       "  17294618,\n",
+       "  170599734,\n",
+       "  6369672,\n",
+       "  230413074,\n",
+       "  101130274,\n",
+       "  476491114,\n",
+       "  9833174,\n",
+       "  129609852,\n",
+       "  4506626,\n",
+       "  1168181],\n",
+       " 'test_result_folder': './test',\n",
+       " 'test_split': ['11',\n",
+       "  '12',\n",
+       "  '13',\n",
+       "  '14',\n",
+       "  '15',\n",
+       "  '16',\n",
+       "  '17',\n",
+       "  '18',\n",
+       "  '19',\n",
+       "  '20',\n",
+       "  '21'],\n",
+       " 'training_split': ['00',\n",
+       "  '01',\n",
+       "  '02',\n",
+       "  '03',\n",
+       "  '04',\n",
+       "  '05',\n",
+       "  '06',\n",
+       "  '07',\n",
+       "  '09',\n",
+       "  '10'],\n",
+       " 'all_split': ['00',\n",
+       "  '01',\n",
+       "  '02',\n",
+       "  '03',\n",
+       "  '04',\n",
+       "  '05',\n",
+       "  '06',\n",
+       "  '07',\n",
+       "  '09',\n",
+       "  '08',\n",
+       "  '10',\n",
+       "  '11',\n",
+       "  '12',\n",
+       "  '13',\n",
+       "  '14',\n",
+       "  '15',\n",
+       "  '16',\n",
+       "  '17',\n",
+       "  '18',\n",
+       "  '19',\n",
+       "  '20',\n",
+       "  '21'],\n",
+       " 'validation_split': ['08'],\n",
+       " 'use_cache': True,\n",
+       " 'sampler': {'name': 'SemSegRandomSampler'}}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cfg['dataset']"
    ]
@@ -248,10 +564,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "ae8461fb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'SemSegRandomSampler'}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cfg.dataset.sampler"
    ]
@@ -266,10 +593,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "39176453",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'SemSegRandomSampler'}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cfg['dataset']['sampler']"
    ]
@@ -279,19 +617,29 @@
    "id": "ddbbcc07",
    "metadata": {},
    "source": [
-    "#### Mutate Parameters\n",
+    "## Mutating Parameters\n",
     "\n",
-    "Please note that this will only alter the properties of `cfg` variable in memory. The original `yml` file on disk remains unchanged."
+    "If you want to change the keys or values in your config files, you may use dictionary synatx as shown below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "5706a884",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NewSampler\n"
+     ]
+    }
+   ],
    "source": [
-    "cfg['dataset']['sampler'] = \"NewSampler\""
+    "cfg['dataset']['sampler'] = \"NewSampler\"\n",
+    "\n",
+    "print(cfg['dataset']['sampler'])"
    ]
   },
   {
@@ -299,7 +647,37 @@
    "id": "63b16241",
    "metadata": {},
    "source": [
-    "As shown above, we may use `cfg.dataset.sampler = \"NewSampler\"` as well!"
+    "We may use object dot notation too. Let us change `dataset_path` using it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "11006162",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "./SemanticKITTI\n"
+     ]
+    }
+   ],
+   "source": [
+    "cfg.dataset.dataset_path = \"./SemanticKITTI\"\n",
+    "\n",
+    "print(cfg.dataset.dataset_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25ab93fe",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    **Note:** Original YAML file on disk remains unchanged!\n",
+    "</div>"
    ]
   },
   {
@@ -307,9 +685,7 @@
    "id": "32338adf",
    "metadata": {},
    "source": [
-    "#### Build Dataset Using Config\n",
-    "\n",
-    "VERIFY: Use `**kwargs`"
+    "## Building Dataset Component"
    ]
   },
   {
@@ -317,17 +693,58 @@
    "id": "6181b4de",
    "metadata": {},
    "source": [
-    "Explicitly create a `dataset` object which will hold all information from the `cfg.dataset` dictionary:"
+    "Look at the code snippet below:\n",
+    "\n",
+    "```py\n",
+    "# Read a dataset by specifying the path, cache directory, training split etc.\n",
+    "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
+    "                                      cache_dir='./logs/cache',\n",
+    "                                      training_split=['00'],\n",
+    "                                      validation_split=['01'],\n",
+    "                                      test_split=['01'])\n",
+    "```\n",
+    "\n",
+    "The `dataset` object is created by explicitly passing **dataset-specific parameters** to the constructor of `ml3d.datasets.SemanticKITTI` class. Instead of passing these parameters one by one manually we may use config files as shown below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "913f9c77",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n",
+      " Using the Open3D PyTorch ops with CUDA 11 may have stability issues!\n",
+      "\n",
+      " We recommend to compile PyTorch from source with compile flags\n",
+      "   '-Xcompiler -fno-gnu-unique'\n",
+      "\n",
+      " or use the PyTorch wheels at\n",
+      "   https://github.com/isl-org/open3d_downloads/releases/tag/torch1.8.2\n",
+      "\n",
+      "\n",
+      " Ignore this message if PyTorch has been compiled with the aforementioned\n",
+      " flags.\n",
+      "\n",
+      " See https://github.com/isl-org/Open3D/issues/3324 and\n",
+      " https://github.com/pytorch/pytorch/issues/52663 for more information on this\n",
+      " problem.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "dataset = ml3d.datasets.SemanticKITTI(cfg.dataset)"
+    "import open3d.ml.torch as ml3d\n",
+    "\n",
+    "dataset = ml3d.datasets.SemanticKITTI(**cfg.dataset)"
    ]
   },
   {
@@ -340,10 +757,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "8abcef5d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'cfg': <open3d._ml3d.utils.config.Config at 0x7fbbe9f96110>,\n",
+       " 'name': 'SemanticKITTI',\n",
+       " 'rng': Generator(PCG64) at 0x7FBBE9D225F0,\n",
+       " 'label_to_names': {0: 'unlabeled',\n",
+       "  1: 'car',\n",
+       "  2: 'bicycle',\n",
+       "  3: 'motorcycle',\n",
+       "  4: 'truck',\n",
+       "  5: 'other-vehicle',\n",
+       "  6: 'person',\n",
+       "  7: 'bicyclist',\n",
+       "  8: 'motorcyclist',\n",
+       "  9: 'road',\n",
+       "  10: 'parking',\n",
+       "  11: 'sidewalk',\n",
+       "  12: 'other-ground',\n",
+       "  13: 'building',\n",
+       "  14: 'fence',\n",
+       "  15: 'vegetation',\n",
+       "  16: 'trunk',\n",
+       "  17: 'terrain',\n",
+       "  18: 'pole',\n",
+       "  19: 'traffic-sign'},\n",
+       " 'num_classes': 20,\n",
+       " 'remap_lut_val': array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  2,  0,  5,  0,  3,  5,\n",
+       "         0,  4,  0,  5,  0,  0,  0,  0,  0,  0,  0,  0,  0,  6,  7,  8,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  9,  0,  0,  0, 10,  0,  0,  0, 11, 12, 13,\n",
+       "        14,  0,  0,  0,  0,  0,  0,  0,  0,  9,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0, 15, 16, 17,  0,  0,  0,  0,  0,  0,  0, 18, 19,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  1,  7,  6,\n",
+       "         8,  5,  5,  4,  5,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0], dtype=int32),\n",
+       " 'remap_lut': array([ 0, 10, 11, 15, 18, 20, 30, 31, 32, 40, 44, 48, 49, 50, 51, 70, 71,\n",
+       "        72, 80, 81,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
+       "         0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
+       "       dtype=int32)}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "vars(dataset)"
    ]
@@ -358,10 +839,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "a05e5090",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'unlabeled',\n",
+       " 1: 'car',\n",
+       " 2: 'bicycle',\n",
+       " 3: 'motorcycle',\n",
+       " 4: 'truck',\n",
+       " 5: 'other-vehicle',\n",
+       " 6: 'person',\n",
+       " 7: 'bicyclist',\n",
+       " 8: 'motorcyclist',\n",
+       " 9: 'road',\n",
+       " 10: 'parking',\n",
+       " 11: 'sidewalk',\n",
+       " 12: 'other-ground',\n",
+       " 13: 'building',\n",
+       " 14: 'fence',\n",
+       " 15: 'vegetation',\n",
+       " 16: 'trunk',\n",
+       " 17: 'terrain',\n",
+       " 18: 'pole',\n",
+       " 19: 'traffic-sign'}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "dataset.label_to_names"
    ]
@@ -371,7 +882,7 @@
    "id": "b89d87c8",
    "metadata": {},
    "source": [
-    "Similarly, we may bulild model and pipline components using `cfg.model` and `cfg.pipeline` respectively. Have a look at training tutorials for code examples."
+    "Similarly, we may bulild **model and pipeline components** using `cfg.model` and `cfg.pipeline` respectively. Have a look at training tutorials for code examples."
    ]
   }
  ],
@@ -391,7 +902,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorial/notebook/02_datasets.ipynb b/docs/tutorial/notebook/02_datasets.ipynb
index 663a61406..4bb1b01cf 100644
--- a/docs/tutorial/notebook/02_datasets.ipynb
+++ b/docs/tutorial/notebook/02_datasets.ipynb
@@ -5,7 +5,7 @@
    "id": "e5b18a95",
    "metadata": {},
    "source": [
-    "## 📚📑Open3D-ML Datasets"
+    "# Open3D-ML Datasets"
    ]
   },
   {
@@ -13,18 +13,23 @@
    "id": "cc08bb58",
    "metadata": {},
    "source": [
-    "In the previous tutorial we briefly discussed what a `dataset` is and how to read build it using config file. In this tutorial, we will dive a bit deeper  on how to read Open3D-ML datasets.\n",
-    "\n",
-    "You may use any dataset available in the `ml3d.datasets` namespace. For this example, we will use the `SemanticKITTI` dataset. However, you must understand that the parameters may vary for each dataset.\n",
-    "\n",
-    "> With Open3D-ML, you may create your own dataset classes! We will talk more about it later ✅\n",
-    "\n",
-    "In this tutorial we will learn\n",
+    "Open3D-ML provides many popular datasets out of the box. In this tutorial, we will see how to use them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aece71cb",
+   "metadata": {},
+   "source": [
+    "## Available datasets\n",
     "\n",
-    "🎯 General structure of point cloud dataset. <br/>\n",
-    "🎯 Structure oF SemanticKITTI dataset and it's dataset-specific class. <br/>\n",
-    "🎯 Accessing dataset splits. <br/>\n",
-    "🎯 Accessing point cloud and it's atributes within splits. <br/>"
+    "You may use any dataset available in the `ml3d.datasets` namespace. Some datasets available in the namespace are: `Argoverse`, `InferenceDummySplit`, `KITTI`, `Lyft`, `MatterportObjects`, `NuScenes`, `ParisLille3D`, `S3DIS`, `Scannet`, `SemSegRandomSampler`, `SemSegSpatiallyRegularSampler`, `Semantic3D`, `SemanticKITTI`, `ShapeNet`, `SunRGBD`, `Toronto3D`, `Custom3D` and `Waymo`.\n",
+    " \n",
+    " For this example, we will use the `SemanticKITTI` dataset. \n",
+    " \n",
+    "<div class=\"alert alert-info\">\n",
+    "    **Note:** Config file parameters may vary for each dataset.\n",
+    "</div>"
    ]
   },
   {
@@ -32,16 +37,16 @@
    "id": "39b3561f",
    "metadata": {},
    "source": [
-    "## 🌟 Generic Dataset: How Does it Look?"
+    "## Point cloud dataset"
    ]
   },
   {
    "attachments": {
-    "image-11.png": {
-     "image/png": 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"
+    "image-12.png": {
+     "image/png": 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"
     },
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"
+    "image-14.png": {
+     "image/png": 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"
     }
    },
    "cell_type": "markdown",
@@ -50,15 +55,17 @@
    "source": [
     "**Point cloud:** A point cloud is a collection of points in 3D space. Every point point in point cloud may has multiple features associated with it. An individual point in point cloud has `(x, y, z)` values to indicate it's position in 3D space for x-axis, y-axis and z-axis respectively. And it may / may not have features associated with it; Most common features associalted with individual points are `(r, g, b, i)` values indicating red color, blue color, green color and intensity measure respectively.\n",
     "\n",
-    "![image-11.png](attachment:image-11.png)\n",
+    "![image-12.png](attachment:image-12.png)\n",
     "\n",
     "Generally speaking, a dataset is a collection of train, validation and test splits where each split has multiple point clouds within them. Model interacts and learns from train and validation point clouds during training phase but never interacts with test point clouds. Test point clouds are considered as out of bag samples and used during evaluation phase only.\n",
     "\n",
-    "![image.png](attachment:image.png)\n",
+    "![image-14.png](attachment:image-14.png)\n",
     "\n",
-    "**Note:** No two point cloud splits share a common point cloud file! This may cause [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)).\n",
+    "Always make sure that no two point cloud splits share a common point cloud file! This may cause [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)).\n",
     "\n",
-    "> 💡 **Under the Hood:** Datasets are built using [BaseDataset](https://github.com/isl-org/Open3D-ML/blob/f424215be133b8c2571e66bbab8fc5c4f2aaa931/ml3d/datasets/base_dataset.py#L12-L100)"
+    "<div class=\"alert alert-info\">\n",
+    "    **Note:** Datasets are built using <a href=\"https://github.com/isl-org/Open3D-ML/blob/f424215be133b8c2571e66bbab8fc5c4f2aaa931/ml3d/datasets/base_dataset.py#L12-L100\">`BaseDataset`</a>\n",
+    "</div>"
    ]
   },
   {
@@ -66,9 +73,9 @@
    "id": "d22689b1",
    "metadata": {},
    "source": [
-    "## 🐱 SemanticKITTI Dataset\n",
+    "## SemanticKITTI dataset\n",
     "\n",
-    "The points (positions & features), point clouds & splits are essential components of any dataset. These individual compontents can be arranged in many possible ways; **To parse them, we need dataset-specific classes**. One such class is `ml3d.datasets.SemanticKITTI` which parses [SemanticKITTI dataset](http://www.semantic-kitti.org/dataset.html).\n",
+    "The points (positions & features), point clouds & splits are essential components of any dataset. Point cloud files can be stored on disk in many possible ways in multiple formats; **To parse them, we need dataset-specific classes**. One such class is `ml3d.datasets.SemanticKITTI` which parses [SemanticKITTI dataset](http://www.semantic-kitti.org/dataset.html).\n",
     "\n",
     "You may download the dataset by running script available at [scripts/download_datasets/download_semantickitti.sh](https://github.com/isl-org/Open3D-ML/blob/master/scripts/download_datasets/download_semantickitti.sh)."
    ]
@@ -93,29 +100,57 @@
    "id": "60ab883b",
    "metadata": {},
    "source": [
-    "Here, the sequence folders `00` and `01` contain point clouds. Now to read them we use `ml3d.datasets.SemanticKITTI` (More information available in [API docs](http://www.open3d.org/docs/release/python_api/open3d.ml.torch.datasets.SemanticKITTI.html))."
+    "Here, the sequence folders `00` and `01` contain point clouds. To read them we use `ml3d.datasets.SemanticKITTI` (More information available in [API docs](http://www.open3d.org/docs/release/python_api/open3d.ml.torch.datasets.SemanticKITTI.html))."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "618f253a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
+      "[Open3D INFO] WebRTC GUI backend enabled.\n",
+      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n",
+      " Using the Open3D PyTorch ops with CUDA 11 may have stability issues!\n",
+      "\n",
+      " We recommend to compile PyTorch from source with compile flags\n",
+      "   '-Xcompiler -fno-gnu-unique'\n",
+      "\n",
+      " or use the PyTorch wheels at\n",
+      "   https://github.com/isl-org/open3d_downloads/releases/tag/torch1.8.2\n",
+      "\n",
+      "\n",
+      " Ignore this message if PyTorch has been compiled with the aforementioned\n",
+      " flags.\n",
+      "\n",
+      " See https://github.com/isl-org/Open3D/issues/3324 and\n",
+      " https://github.com/pytorch/pytorch/issues/52663 for more information on this\n",
+      " problem.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
-    "# import torch\n",
     "import open3d.ml.torch as ml3d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "9739b5cf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# VERIFY: Download to dataset_path?\n",
-    "    \n",
     "# Read a dataset by specifying the path. We are also providing the cache directory and splits.\n",
     "dataset = ml3d.datasets.SemanticKITTI(dataset_path='SemanticKITTI/',\n",
     "                                      cache_dir='./logs/cache',\n",
@@ -141,7 +176,7 @@
    "id": "f0e2077e",
    "metadata": {},
    "source": [
-    "### 🗡️ Accessing Splits & Number of Point Clouds Within."
+    "## Accessing splits"
    ]
   },
   {
@@ -167,10 +202,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "83a166e2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'SemanticKITTI/dataset/sequences/00/velodyne'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_12788/3269926996.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_split\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'training'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/usr/local/pip-global/open3d/_ml3d/datasets/semantickitti.py\u001b[0m in \u001b[0;36mget_split\u001b[0;34m(self, split)\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0mA\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0msplit\u001b[0m \u001b[0mobject\u001b[0m \u001b[0mproviding\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mrequested\u001b[0m \u001b[0msubset\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \"\"\"\n\u001b[0;32m--> 142\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSemanticKITTISplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mis_tested\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/pip-global/open3d/_ml3d/datasets/semantickitti.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, split)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'training'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m         log.info(\"Found {} pointclouds for {}\".format(len(self.path_list),\n\u001b[1;32m    262\u001b[0m                                                       split))\n",
+      "\u001b[0;32m/usr/local/pip-global/open3d/_ml3d/datasets/base_dataset.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, split)\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'training'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcfg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m         \u001b[0mpath_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_split_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpath_list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msplit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/usr/local/pip-global/open3d/_ml3d/datasets/semantickitti.py\u001b[0m in \u001b[0;36mget_split_list\u001b[0;34m(self, split)\u001b[0m\n\u001b[1;32m    248\u001b[0m                            'velodyne')\n\u001b[1;32m    249\u001b[0m             file_list.append(\n\u001b[0;32m--> 250\u001b[0;31m                 [join(pc_path, f) for f in np.sort(os.listdir(pc_path))])\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mfile_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'SemanticKITTI/dataset/sequences/00/velodyne'"
+     ]
+    }
+   ],
    "source": [
     "train_split = dataset.get_split('training')"
    ]
@@ -263,7 +314,9 @@
    "id": "2c70fa37",
    "metadata": {},
    "source": [
-    "> 💡 **Under the Hood:** The returned split objects are built using [BaseDatasetSplit](https://github.com/isl-org/Open3D-ML/blob/f424215be133b8c2571e66bbab8fc5c4f2aaa931/ml3d/datasets/base_dataset.py#L103-L148)."
+    "<div class=\"alert alert-info\">\n",
+    "    **Note:** The returned split objects are built using <a href=\"https://github.com/isl-org/Open3D-ML/blob/f424215be133b8c2571e66bbab8fc5c4f2aaa931/ml3d/datasets/base_dataset.py#L103-L148\">`BaseDatasetSplit`</a>\n",
+    "</div>"
    ]
   },
   {
@@ -271,7 +324,7 @@
    "id": "737a611f",
    "metadata": {},
    "source": [
-    "### 🌨️ Points & Point Clouds within Splits"
+    "### Point clouds within splits"
    ]
   },
   {
@@ -295,15 +348,7 @@
    "id": "b484107b",
    "metadata": {},
    "source": [
-    "Maximum integer ID possible for `get_attr` and `get_data` can be calculated using:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d35892f8",
-   "metadata": {},
-   "source": [
-    "len(val_split)-1"
+    "Maximum integer ID possible for `get_attr` and `get_data` can be calculated using `len(split)-1`."
    ]
   },
   {
@@ -359,9 +404,6 @@
    "id": "158a6796",
    "metadata": {},
    "source": [
-    "\n",
-    "![dataset_coordinates](https://user-images.githubusercontent.com/93158890/162549410-6369cbd0-b835-4216-ba54-945e3f591395.jpg)\n",
-    "\n",
     "- The **`'point'`** key value contains a set of 3D point coordinates - X, Y, and Z:\n",
     "\n",
     "- The **`'feat'`** (features) key value contains RGB color information for each of the above points.\n",
@@ -399,7 +441,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/tutorial/notebook/03_train_model.ipynb b/docs/tutorial/notebook/03_train_model.ipynb
index d338333a9..d2b2728bd 100644
--- a/docs/tutorial/notebook/03_train_model.ipynb
+++ b/docs/tutorial/notebook/03_train_model.ipynb
@@ -5,7 +5,7 @@
    "id": "e7cd83b2",
    "metadata": {},
    "source": [
-    "## 🚂🚂Training a Semantic Segmentation Model"
+    "## Training a Semantic Segmentation Model"
    ]
   },
   {
@@ -15,8 +15,11 @@
    "source": [
     "In this tutorial, we will learn how to train a semantic segmentation model using either PyTorch or Tensorflow. \n",
     "\n",
-    "Before you begin, ensure that you have the right dependencies installed in your enviroment. The requirements text files are present in the main directory of Open3D-ML respoitory.\n",
+    "## Installing dependencies\n",
     "\n",
+    "Before you start, chose either Pyorch or Tensorflow. You cannot chose both. The requirements text files are present in the main directory of Open3D-ML respoitory. Use them to ensure that you have the right dependencies installed in your enviroment. \n",
+    "\n",
+    "From Open3D-ML main directory,\n",
     "\n",
     "PyTorch users may run:\n",
     "```sh\n",
@@ -28,28 +31,17 @@
     "pip install -r requirements-tensoflow.txt\n",
     "```\n",
     "\n",
-    "\n",
-    "In this tutorial, we will learn:\n",
-    "\n",
-    "🎯 Downloading datasets available in open3D-ML. <br/>\n",
-    "🎯 Build dataset, model and pipeline from config file. </br>\n",
-    "🎯 Training a model using train and validation splits. <br/>\n",
-    "🎯 Evaluating perfromance on test dataset. <br/>\n",
-    "🎯 Running inference on dataset splits or novel point cloud data <br/>\n",
-    "🎯 Restoring weights from checkpoints."
+    "Create a folder where your dataset will be downloaded."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "af472b00",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create a temporary working directory.\n",
-    "# Note: make sure you have sufficient write permissions.\n",
-    "import shutil\n",
-    "shutil.mkdir('/tmp/o3dml')"
+    "!mkdir -p data"
    ]
   },
   {
@@ -57,7 +49,7 @@
    "id": "c61cdb80",
    "metadata": {},
    "source": [
-    "### ⏬ Downloading Dataset"
+    "### Downloading Toronto3D dataset"
    ]
   },
   {
@@ -72,12 +64,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "a7f719b8",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile /tmp/o3dml/download_toronto3d.sh\n",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing data/download_toronto3d.sh\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile data/download_toronto3d.sh\n",
     "#!/bin/bash\n",
     "  \n",
     "if [ \"$#\" -ne 1 ]; then\n",
@@ -118,14 +118,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "775f199e",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%shell\n",
-    "\n",
-    "bash /tmp/o3dml/download_toronto3d.sh /tmp/o3dml/toronto3d_dataset"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: timestamping does nothing in combination with -O. See the manual\n",
+      "for details.\n",
+      "\n",
+      "--2022-06-14 22:46:55--  https://xx9lca.sn.files.1drv.com/y4mUm9-LiY3vULTW79zlB3xp0wzCPASzteId4wdUZYpzWiw6Jp4IFoIs6ADjLREEk1-IYH8KRGdwFZJrPlIebwytHBYVIidsCwkHhW39aQkh3Vh0OWWMAcLVxYwMTjXwDxHl-CDVDau420OG4iMiTzlsK_RTC_ypo3z-Adf-h0gp2O8j5bOq-2TZd9FD1jPLrkf3759rB-BWDGFskF3AsiB3g\n",
+      "Resolving xx9lca.sn.files.1drv.com (xx9lca.sn.files.1drv.com)... 13.107.42.12\n",
+      "Connecting to xx9lca.sn.files.1drv.com (xx9lca.sn.files.1drv.com)|13.107.42.12|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1143871417 (1.1G) [application/zip]\n",
+      "Saving to: ‘data/toronto3d_dataset/Toronto3D/Toronto_3D.zip’\n",
+      "\n",
+      "data/toronto3d_data 100%[===================>]   1.06G  20.0MB/s    in 67s     \n",
+      "\n",
+      "2022-06-14 22:48:03 (16.2 MB/s) - ‘data/toronto3d_dataset/Toronto3D/Toronto_3D.zip’ saved [1143871417/1143871417]\n",
+      "\n",
+      "Archive:  Toronto_3D.zip\n",
+      "  inflating: Colors.xml              \n",
+      "  inflating: L001.ply                \n",
+      "  inflating: L002.ply                \n",
+      "  inflating: L003.ply                \n",
+      "  inflating: L004.ply                \n",
+      "  inflating: Mavericks_classes_9.txt  \n"
+     ]
+    }
+   ],
+   "source": [
+    "!bash data/download_toronto3d.sh data/toronto3d_dataset"
    ]
   },
   {
@@ -136,7 +162,7 @@
     "You may see downloaded point cloud files as shown below:\n",
     "\n",
     "```\n",
-    "/tmp/o3d/toronto3d\n",
+    "/data/toronto3d\n",
     "└── Toronto3D\n",
     "    ├── Colors.xml\n",
     "    ├── L001.ply\n",
@@ -149,35 +175,16 @@
     "```"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8a3cf348",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%shell\n",
-    "\n",
-    "tree /tmp/o3dml/toronto3d_dataset"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "c1ec1929",
    "metadata": {},
    "source": [
-    "> ### (Tesorflow Only) Limit Memory Usage ⚙️\n",
-    "> \n",
-    "> TensorFlow maps nearly all of GPU memory by default. This may result in out_of_memory error if some of the ops allocate memory independent to tensorflow. You may want to limit memory usage as and when needed by the process. Use following code right after importing tensorflow:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1bb347c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "## Limiting memory usage (Tesorflow Users)\n",
+    "\n",
+    "TensorFlow maps nearly all of GPU memory by default. This may result in out_of_memory error if some of the ops allocate memory independent to tensorflow. You may want to limit memory usage as and when needed by the process. Use following code right after importing tensorflow:\n",
+    "\n",
+    "```python\n",
     "import tensorflow as tf\n",
     "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
     "if gpus:\n",
@@ -185,7 +192,8 @@
     "        for gpu in gpus:\n",
     "            tf.config.experimental.set_memory_growth(gpu, True)\n",
     "    except RuntimeError as e:\n",
-    "        print(e)"
+    "        print(e)\n",
+    "```"
    ]
   },
   {
@@ -193,7 +201,7 @@
    "id": "4b955f11",
    "metadata": {},
    "source": [
-    "### 🧩 Build Essential Componets for Training\n",
+    "## Building componets for training\n",
     "\n",
     "First, let us do some basic imports. Import statements are depend on what framework you are using - Pytorch or Tensorflow."
    ]
@@ -203,19 +211,13 @@
    "id": "a4dfca8e",
    "metadata": {},
    "source": [
-    "Tesorflow users may run:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f10e33a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "Tesorflow users may run:\n",
+    "\n",
+    "```py\n",
     "import open3d.ml.tf as ml3d\n",
     "from open3d.ml.tf.models import RandLANet\n",
-    "from open3d.ml.tf.pipelines import SemanticSegmentation"
+    "from open3d.ml.tf.pipelines import SemanticSegmentation\n",
+    "```"
    ]
   },
   {
@@ -228,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "id": "8bcaac2a",
    "metadata": {},
    "outputs": [],
@@ -243,7 +245,7 @@
    "id": "a2861200",
    "metadata": {},
    "source": [
-    "> The goal was to create `ml3d`, `RandLANet` and `SemanticSegmentation` based on either PyTorch or Tensorflow framework."
+    "The goal was to create `ml3d`, `RandLANet` and `SemanticSegmentation` based on either PyTorch or Tensorflow framework."
    ]
   },
   {
@@ -256,16 +258,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "e8319720",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%writefile /tmp/o3dml/randlanet_toronto3d.yml\n",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting data/randlanet_toronto3d.yml\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile data/randlanet_toronto3d.yml\n",
     "dataset:\n",
     "  name: Toronto3D\n",
     "  cache_dir: ./logs/cache\n",
-    "  dataset_path: /tmp/o3d/toronto3d/Toronto3D # path/to/your/dataset\n",
+    "  dataset_path: data/toronto3d_dataset/Toronto3D # path/to/your/dataset\n",
     "  class_weights: [41697357, 1745448, 6572572, 19136493, 674897, 897825, 4634634, 374721]\n",
     "  ignored_label_inds:\n",
     "  - 0\n",
@@ -327,19 +337,19 @@
    "id": "2178391e",
    "metadata": {},
    "source": [
-    "Now, let us load/read the config file:"
+    "Load the config file:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "031d8528",
    "metadata": {},
    "outputs": [],
    "source": [
     "from open3d.ml import utils\n",
     "\n",
-    "cfg_file = \"/tmp/o3dml/randlanet_toronto3d.yml\"\n",
+    "cfg_file = \"data/randlanet_toronto3d.yml\"\n",
     "cfg = utils.Config.load_from_file(cfg_file)"
    ]
   },
@@ -353,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "beb9aa91",
    "metadata": {},
    "outputs": [],
@@ -371,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "5f3d73b9",
    "metadata": {},
    "outputs": [],
@@ -389,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "6a965c22",
    "metadata": {},
    "outputs": [],
@@ -404,7 +414,7 @@
    "id": "352e623d",
    "metadata": {},
    "source": [
-    "### 🛤️ Training The Model"
+    "### Training the model"
    ]
   },
   {
@@ -412,7 +422,18 @@
    "execution_count": null,
    "id": "c88c974d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO - 2022-06-14 23:01:41,840 - semantic_segmentation - DEVICE : cpu\n",
+      "INFO - 2022-06-14 23:01:41,842 - semantic_segmentation - Logging in file : ./logs/RandLANet_Toronto3D_torch/log_train_2022-06-14_23:01:41.txt\n",
+      "INFO - 2022-06-14 23:01:41,844 - toronto3d - Found 3 pointclouds for train\n",
+      "preprocess:   0%|                                                | 0/3 [00:00<?, ?it/s]"
+     ]
+    }
+   ],
    "source": [
     "pipeline.run_train()"
    ]
@@ -432,7 +453,7 @@
    "id": "c8a944ff",
    "metadata": {},
    "source": [
-    "### 🧑‍🏫 Model Evaluation\n",
+    "## Evaluation on holdout split\n",
     "\n",
     "evaluate the trained model on the test split by calling the `run_test()` method:"
    ]
@@ -442,7 +463,20 @@
    "execution_count": null,
    "id": "d84b47cd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO - 2022-06-15 00:43:22,308 - semantic_segmentation - DEVICE : cpu\n",
+      "INFO - 2022-06-15 00:43:22,309 - semantic_segmentation - Logging in file : ./logs/RandLANet_Toronto3D_torch/log_test_2022-06-15_00:43:22.txt\n",
+      "INFO - 2022-06-15 00:43:22,311 - toronto3d - Found 1 pointclouds for test\n",
+      "INFO - 2022-06-15 00:43:25,911 - semantic_segmentation - Initializing from scratch.\n",
+      "INFO - 2022-06-15 00:43:25,912 - semantic_segmentation - Started testing\n",
+      "test 0/1:  75%|████████████████████▉       | 3731827/4990714 [10:25<05:27, 3846.59it/s]"
+     ]
+    }
+   ],
    "source": [
     "pipeline.run_test()"
    ]
@@ -452,9 +486,9 @@
    "id": "d7d3ebc7",
    "metadata": {},
    "source": [
-    "### ✅ Inference on Individual Point Clouds\n",
+    "## Running inference on individual point cloud\n",
     " \n",
-    "`pipeline.run_inference` method let's us make predictions on novel point cloud data. It takes a dictionary with `point`, `feat` and `label` keys as input."
+    "`pipeline.run_inference` method lets us make predictions on novel point cloud data. It takes a dictionary with `point`, `feat` and `label` keys as input."
    ]
   },
   {
@@ -490,7 +524,9 @@
    "id": "ce33b688",
    "metadata": {},
    "source": [
-    "> 💡 You may replace `data` with custom point cloud data."
+    "<div class=\"alert alert-info\">\n",
+    "**Note:** You may replace `data` dictionary with custom point cloud data. \n",
+    "</div>"
    ]
   },
   {
@@ -498,7 +534,7 @@
    "id": "f99e7a15",
    "metadata": {},
    "source": [
-    "### 🔃 Restoring from Checkoints"
+    "## Restoring from checkoints"
    ]
   },
   {
@@ -506,7 +542,7 @@
    "id": "2e916d4c",
    "metadata": {},
    "source": [
-    "In the above example, you may notice that we are using latest trained model to make predictions. Latest trained models may not always be the best model, so you may want to use model from previous checkpoints instead. \n",
+    "In the above example, you may notice that we are using latest trained model to make predictions. Latest trained models may not always be the best model so you may want to use model from previous checkpoints instead. \n",
     "\n",
     "`pipeline` provides `load_ckpt` method to restore model weights from training checkpoints."
    ]
@@ -539,7 +575,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.7.13"
   }
  },
  "nbformat": 4,

From a80523e41cc0fc7e532b5987392fbed9d0b29127 Mon Sep 17 00:00:00 2001
From: Asapanna Rakesh <45640029+INF800@users.noreply.github.com>
Date: Tue, 21 Jun 2022 14:28:00 +0000
Subject: [PATCH 4/5] use _ml3d, re-write a cell in 01_config_files.ipynb

---
 docs/tutorial/notebook/01_config_files.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/docs/tutorial/notebook/01_config_files.ipynb b/docs/tutorial/notebook/01_config_files.ipynb
index cee93b8e6..0920fd86f 100644
--- a/docs/tutorial/notebook/01_config_files.ipynb
+++ b/docs/tutorial/notebook/01_config_files.ipynb
@@ -79,9 +79,9 @@
     }
    ],
    "source": [
-    "from open3d.ml import utils\n",
+    "import open3d.ml as _ml3d\n",
     "\n",
-    "cfg = utils.Config.load_from_file(cfg_file)"
+    "cfg = _ml3d.utils.Config.load_from_file(cfg_file)"
    ]
   },
   {
@@ -89,7 +89,7 @@
    "id": "7fe103b0",
    "metadata": {},
    "source": [
-    "`utils.Config.load_from_file` takes path to config file as input and returns `Config`. \n",
+    "`_ml3d.utils.Config.load_from_file(cfg_file)` takes path to config file as input and returns `Config`. \n",
     "\n",
     "<div class=\"alert alert-info\">\n",
     "**Note:**  To avoid `FileNotFoundError`, always make sure that the config file exists at the given location. The config file must be a valid YAML file!  </div>"
@@ -361,7 +361,7 @@
    "id": "1aa31b4e",
    "metadata": {},
    "source": [
-    "To access any property, use either `cfg.{property_name}` or `cfg['{property_name}']` syntax as in object **dot notation**.\n",
+    "You can access configuration values as object attributes (`cfg.{property_name}`) or dictionary key values (`cfg['{property_name}']`).\n",
     "\n",
     "For example, `dataset` dictionary can be accessed using the following code (same can be done for `model` and `pipeline`)"
    ]

From 06e9ae2e38dd3cc5141c88d1c772a1d26b41c0f8 Mon Sep 17 00:00:00 2001
From: Asapanna Rakesh <45640029+INF800@users.noreply.github.com>
Date: Tue, 21 Jun 2022 14:47:23 +0000
Subject: [PATCH 5/5] add more content to md cells

---
 docs/tutorial/notebook/02_datasets.ipynb | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/docs/tutorial/notebook/02_datasets.ipynb b/docs/tutorial/notebook/02_datasets.ipynb
index 4bb1b01cf..be0724b36 100644
--- a/docs/tutorial/notebook/02_datasets.ipynb
+++ b/docs/tutorial/notebook/02_datasets.ipynb
@@ -5,7 +5,7 @@
    "id": "e5b18a95",
    "metadata": {},
    "source": [
-    "# Open3D-ML Datasets"
+    "# Datasets"
    ]
   },
   {
@@ -55,12 +55,17 @@
    "source": [
     "**Point cloud:** A point cloud is a collection of points in 3D space. Every point point in point cloud may has multiple features associated with it. An individual point in point cloud has `(x, y, z)` values to indicate it's position in 3D space for x-axis, y-axis and z-axis respectively. And it may / may not have features associated with it; Most common features associalted with individual points are `(r, g, b, i)` values indicating red color, blue color, green color and intensity measure respectively.\n",
     "\n",
+    "There are no hard and fast rules to decide what values go into features.\n",
+    "Datasets may instead use **r** (lidar reflectance) as feature, or no features at all.\n",
+    "\n",
     "![image-12.png](attachment:image-12.png)\n",
     "\n",
     "Generally speaking, a dataset is a collection of train, validation and test splits where each split has multiple point clouds within them. Model interacts and learns from train and validation point clouds during training phase but never interacts with test point clouds. Test point clouds are considered as out of bag samples and used during evaluation phase only.\n",
     "\n",
     "![image-14.png](attachment:image-14.png)\n",
     "\n",
+    "A dataset is partitioned into train(ing), valid(ation) and test(ing) splits. Models use data from the training split to update network weights and performance is measured on the validation split. Hyperparameters can be adjusted to optimize performance on the validation split. The test split is never used during the training step and is only used to report the final performance.\n",
+    "\n",
     "Always make sure that no two point cloud splits share a common point cloud file! This may cause [data leakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)).\n",
     "\n",
     "<div class=\"alert alert-info\">\n",
@@ -441,7 +446,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.13"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,