feat: add Vision Transformer (ViT) implementation for image classification

[devvratpathak]

Description

This PR adds a comprehensive Vision Transformer (ViT) implementation to the computer_vision folder for image classification tasks.

Implementation Details

Implementation of the Vision Transformer architecture from "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale" (Dosovitskiy et al., 2020).

Core Components:

• ✅ Patch Embedding: Splits images into non-overlapping patches
• ✅ Linear Projection: Projects flattened patches to embedding dimension
• ✅ Positional Encoding: Adds learnable positional embeddings with CLS token
• ✅ Attention Mechanism: Scaled dot-product attention
• ✅ Layer Normalization: Normalizes layer outputs
• ✅ Feed-Forward Network: Position-wise FFN with GELU activation
• ✅ Transformer Encoder Block: Complete encoder with multi-head attention
• ✅ Vision Transformer Pipeline: Full ViT for image classification

Code Quality:

All functions include comprehensive docstrings, type hints, doctests, and follow repository coding standards. All ruff checks pass successfully.

Example Usage:

from computer_vision.vision_transformer import vision_transformer
import numpy as np

image = np.random.rand(224, 224, 3)
logits = vision_transformer(image, num_classes=1000)
predicted_class = np.argmax(logits)

[algorithms-keeper]

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