diff --git a/concat_fix_repro.py b/concat_fix_repro.py
new file mode 100644
index 00000000000000..53f77b2f762d05
--- /dev/null
+++ b/concat_fix_repro.py
@@ -0,0 +1,49 @@
+import tensorflow as tf
+
+class TestModel(tf.keras.Model):
+
+    def __init__(self):
+        super().__init__()
+        self.d1 = tf.keras.layers.Dense(64, activation='relu')
+        self.d2 = tf.keras.layers.Dense(32)
+        self.d3 = tf.keras.layers.Dense(16)
+
+    def call(self, x, indices=None):
+        x = self.d1(x)
+        if indices is not None:
+            (unique_vals, _) = tf.unique(indices)
+            x = tf.nn.relu(tf.gather(x, unique_vals))
+        else:
+            x = tf.nn.relu(x)
+        partitioned = tf.dynamic_partition(x, tf.cast(tf.reduce_sum(x, axis=1) > 0, tf.int32), num_partitions=2)
+        x = tf.concat(partitioned, axis=0)
+        (top_k_values, _) = tf.nn.top_k(x, k=tf.shape(x)[0] // 2)
+        x = tf.nn.relu(self.d2(top_k_values))
+        return self.d3(x)
+
+
+def get_default_model():
+    return TestModel()
+
+
+def get_sample_inputs():
+    x = tf.random.normal([10, 64])
+    indices = tf.random.uniform([10], maxval=5, dtype=tf.int32)
+    return (x, indices)
+
+
+def main():
+    model = get_default_model()
+    inputs = get_sample_inputs()
+    eager_out = model(*inputs)
+    print('Eager Input shape:', inputs[0].shape)
+    print('Eager Output shape:', eager_out.shape)
+    @tf.function(jit_compile=True)
+    def compiled_forward(*args):
+        return model(*args)
+    compiled_out = compiled_forward(*inputs)
+    print('XLA Output shape:', compiled_out.shape)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tensorflow/compiler/tf2xla/kernels/concat_dynamic_test.py b/tensorflow/compiler/tf2xla/kernels/concat_dynamic_test.py
new file mode 100644
index 00000000000000..60981dc367e0a4
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/concat_dynamic_test.py
@@ -0,0 +1,83 @@
+"""Test for dynamic shape preservation in concat operation with XLA compilation."""
+
+import tensorflow as tf
+import unittest
+
+
+class ConcatDynamicShapeTest(unittest.TestCase):
+
+  def test_dynamic_partition_concat_topk_matmul_xla(self):
+    """Test that XLA preserves dynamic shapes through the full pipeline."""
+
+    class TestModel(tf.keras.Model):
+      def __init__(self):
+        super().__init__()
+        self.d1 = tf.keras.layers.Dense(64, activation='relu')
+        self.d2 = tf.keras.layers.Dense(32)
+        self.d3 = tf.keras.layers.Dense(16)
+
+      def call(self, x, indices=None):
+        x = self.d1(x)
+        if indices is not None:
+          (unique_vals, _) = tf.unique(indices)
+          x = tf.nn.relu(tf.gather(x, unique_vals))
+        else:
+          x = tf.nn.relu(x)
+        
+        # This chain should preserve dynamic dimensions
+        partitioned = tf.dynamic_partition(
+            x, tf.cast(tf.reduce_sum(x, axis=1) > 0, tf.int32), 
+            num_partitions=2)
+        x = tf.concat(partitioned, axis=0)  # Critical: must preserve dynamic dim
+        (top_k_values, _) = tf.nn.top_k(x, k=tf.shape(x)[0] // 2)
+        x = tf.nn.relu(self.d2(top_k_values))
+        return self.d3(x)
+
+    model = TestModel()
+    x = tf.random.normal([10, 64])
+    indices = tf.random.uniform([10], maxval=5, dtype=tf.int32)
+    
+    # Test eager execution
+    eager_out = model(x, indices)
+    self.assertEqual(len(eager_out.shape), 2)
+    self.assertEqual(eager_out.shape[1], 16)
+    
+    # Test XLA compilation - this should not fail with shape errors
+    @tf.function(jit_compile=True)
+    def compiled_forward(x_input, indices_input):
+      return model(x_input, indices_input)
+    
+    # This should succeed without Matrix size-incompatible error
+    compiled_out = compiled_forward(x, indices)
+    
+    # Verify output shapes match between eager and compiled
+    self.assertEqual(eager_out.shape[1], compiled_out.shape[1])
+
+  def test_concat_preserves_dynamic_dimensions(self):
+    """Direct test of concat with dynamic partition outputs."""
+    
+    @tf.function(jit_compile=True)
+    def test_concat_dynamic():
+      x = tf.random.normal([8, 32])
+      partitions = tf.cast(tf.reduce_sum(x, axis=1) > 0, tf.int32)
+      
+      # Create dynamic partition outputs
+      partitioned = tf.dynamic_partition(x, partitions, num_partitions=2)
+      
+      # Concat should preserve dynamic dimension
+      result = tf.concat(partitioned, axis=0)
+      
+      return result, tf.shape(result)
+    
+    result, shape = test_concat_dynamic()
+    
+    # Should succeed without errors and have reasonable output shape
+    self.assertEqual(len(result.shape), 2)
+    self.assertEqual(result.shape[1], 32)
+    self.assertGreater(shape[0], 0)  # Dynamic first dimension
+
+
+if __name__ == '__main__':
+  # Run with eager execution to verify test logic
+  tf.config.run_functions_eagerly(False)
+  unittest.main()
diff --git a/tensorflow/compiler/tf2xla/kernels/concat_op.cc b/tensorflow/compiler/tf2xla/kernels/concat_op.cc
index bed3479941ca41..f85d716a5661d1 100644
--- a/tensorflow/compiler/tf2xla/kernels/concat_op.cc
+++ b/tensorflow/compiler/tf2xla/kernels/concat_op.cc
@@ -94,7 +94,44 @@ class ConcatBaseOp : public XlaOpKernel {
     }
 
     VLOG(1) << "Concat dim " << concat_dim << " equivalent to " << axis;
-    ctx->SetOutput(0, xla::ConcatInDim(ctx->builder(), input_data, axis));
+    xla::XlaOp result = xla::ConcatInDim(ctx->builder(), input_data, axis);
+
+    // Preserve dynamic dimension information for the concat axis.
+    // If any input has a dynamic dimension along `axis`, compute the
+    // summed size (mixing dynamic GetDimensionSize() and static constants)
+    // and set it on the concat result using SetDimensionSize.
+    bool any_input_dynamic = false;
+    xla::XlaOp dynamic_size_sum;
+    bool size_sum_initialized = false;
+
+    for (int i = 0; i < N; ++i) {
+      // Query the input XLA shape to see if this dimension is dynamic.
+      auto input_shape_or = ctx->InputXlaShape(i);
+      OP_REQUIRES_OK(ctx, input_shape_or.status());
+      const xla::Shape& input_shape = *input_shape_or;
+
+      xla::XlaOp input_size;
+      if (input_shape.is_dynamic_dimension(axis)) {
+        any_input_dynamic = true;
+        input_size = xla::GetDimensionSize(input_data[i], axis);
+      } else {
+        input_size = xla::ConstantR0<int32_t>(ctx->builder(),
+                                              input_shape.dimensions(axis));
+      }
+
+      if (!size_sum_initialized) {
+        dynamic_size_sum = input_size;
+        size_sum_initialized = true;
+      } else {
+        dynamic_size_sum = xla::Add(dynamic_size_sum, input_size);
+      }
+    }
+
+    if (any_input_dynamic && N > 0) {
+      result = xla::SetDimensionSize(result, dynamic_size_sum, axis);
+    }
+
+    ctx->SetOutput(0, result);
   }
 
  private:
diff --git a/tensorflow/compiler/tf2xla/kernels/issue_105133_fix_demo.py b/tensorflow/compiler/tf2xla/kernels/issue_105133_fix_demo.py
new file mode 100644
index 00000000000000..9836e97a2d260d
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/issue_105133_fix_demo.py
@@ -0,0 +1,232 @@
+"""Integration demo for GitHub issue #105133: XLA compatibility with conditional operations.
+
+This script reproduces the exact issue and demonstrates the fix.
+
+REQUIREMENTS:
+- TensorFlow 2.20.0+ with XLA/GPU support enabled
+- Run on XLA-enabled build (GPU recommended)
+
+USAGE:
+  python issue_105133_fix_demo.py
+
+This is an integration test - for CI-safe unit tests, see xla_conditional_compatibility_test.py
+
+Issue: OperatorNotAllowedInGraphError when using tf.shape()[0] in Python conditionals with jit_compile=True
+Fix: Replace Python 'if' statements with tf.cond() for XLA compatibility
+"""
+
+import tensorflow as tf
+
+
+class TestModelProblematic(tf.keras.Model):
+    """Original problematic model from issue #105133."""
+
+    def __init__(self):
+        super().__init__()
+        self.conv1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))
+        self.pool1 = tf.keras.layers.MaxPooling2D((2, 2))
+        self.flatten = tf.keras.layers.Flatten()
+        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
+        self.dense2 = tf.keras.layers.Dense(10, activation='softmax')
+
+    def call(self, x):
+        # These Python if statements cause OperatorNotAllowedInGraphError in XLA
+        if tf.shape(x)[0] >= 1:  # PROBLEMATIC
+            x = tf.stop_gradient(x)
+        (h, w) = (tf.shape(x)[1], tf.shape(x)[2])
+        if h > 1 and w > 1:  # PROBLEMATIC  
+            x = self.conv1(x)
+            x = self.pool1(x)
+        else:
+            x = tf.nn.avg_pool2d(x, ksize=2, strides=2, padding='VALID')
+        x = self.flatten(x)
+        flat_size = tf.size(x)
+        if flat_size == 1024:  # PROBLEMATIC
+            x = self.dense1(x)
+        else:
+            x = tf.nn.dropout(x, rate=0.5)
+            x = self.dense2(x)
+        return x
+
+
+class TestModelFixed(tf.keras.Model):
+    """Fixed model using tf.cond for XLA compatibility."""
+
+    def __init__(self):
+        super().__init__()
+        self.conv1 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))
+        self.pool1 = tf.keras.layers.MaxPooling2D((2, 2))
+        self.flatten = tf.keras.layers.Flatten()
+        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
+        self.dense2 = tf.keras.layers.Dense(10, activation='softmax')
+
+    def call(self, x):
+        # Use tf.cond instead of Python if statements for XLA compatibility
+        x = tf.cond(
+            tf.shape(x)[0] >= 1,
+            lambda: tf.stop_gradient(x),
+            lambda: x
+        )
+        
+        h, w = tf.shape(x)[1], tf.shape(x)[2]
+        x = tf.cond(
+            tf.logical_and(h > 1, w > 1),
+            lambda: self._conv_pool_branch(x),
+            lambda: tf.nn.avg_pool2d(x, ksize=2, strides=2, padding='VALID')
+        )
+        
+        x = self.flatten(x)
+        flat_size = tf.size(x)
+        x = tf.cond(
+            tf.equal(flat_size, 1024),
+            lambda: self.dense1(x),
+            lambda: self.dense2(tf.nn.dropout(x, rate=0.5))
+        )
+        
+        return x
+    
+    def _conv_pool_branch(self, x):
+        """Helper method for convolution + pooling branch."""
+        x = self.conv1(x)
+        return self.pool1(x)
+
+
+def get_default_model_problematic():
+    return TestModelProblematic()
+
+
+def get_default_model_fixed():
+    return TestModelFixed()
+
+
+def get_sample_inputs():
+    x = tf.random.normal([16, 28, 28, 1])
+    return (x,)
+
+
+def test_problematic_version():
+    """Test the problematic version - this demonstrates the original issue."""
+    print("Testing problematic version...")
+    model = get_default_model_problematic()
+    inputs = get_sample_inputs()
+    
+    # This works in eager execution
+    eager_out = model(*inputs)
+    print('Problematic - Eager Input shape:', inputs[0].shape)
+    print('Problematic - Eager Output shape:', eager_out.shape)
+    
+    # This should fail with OperatorNotAllowedInGraphError when jit_compile=True
+    @tf.function(jit_compile=True)
+    def compiled_forward(*args):
+        return model(*args)
+    
+    try:
+        compiled_out = compiled_forward(*inputs)
+        print('Problematic - XLA Output shape:', compiled_out.shape)
+        print("⚠️  WARNING: Expected OperatorNotAllowedInGraphError but compilation succeeded!")
+        print("    This may indicate XLA is not enabled or a different TF version.")
+    except Exception as e:
+        error_msg = str(e)
+        expected_keywords = ['symbolic', 'python bool', 'not allowed', 'operatornotallowed']
+        
+        if any(keyword in error_msg.lower() for keyword in expected_keywords):
+            print(f"✓ Expected error caught: {type(e).__name__}")
+            print(f"  Error indicates symbolic tensor used as Python bool")
+        else:
+            print(f"? Unexpected error type: {type(e).__name__}")
+            print(f"  Error message: {error_msg}")
+            print("  This may be related to the symbolic tensor issue or XLA availability.")
+
+
+def test_fixed_version():
+    """Test the fixed version - this demonstrates the solution."""
+    print("\nTesting fixed version...")
+    model = get_default_model_fixed()
+    inputs = get_sample_inputs()
+    
+    # Test eager execution
+    eager_out = model(*inputs)
+    print('Fixed - Eager Input shape:', inputs[0].shape)
+    print('Fixed - Eager Output shape:', eager_out.shape)
+    
+    # Test XLA compilation - this should now work
+    @tf.function(jit_compile=True)
+    def compiled_forward(*args):
+        return model(*args)
+    
+    try:
+        compiled_out = compiled_forward(*inputs)
+        print('Fixed - XLA Output shape:', compiled_out.shape)
+        print("✓ SUCCESS: XLA compilation worked with tf.cond!")
+        
+        # Verify deterministic behavior
+        if eager_out.shape == compiled_out.shape:
+            print("✓ Output shapes match between eager and XLA execution")
+        else:
+            print(f"✗ Shape mismatch: eager {eager_out.shape} vs XLA {compiled_out.shape}")
+        
+        # Test numerical consistency (shapes should be deterministic)
+        if eager_out.dtype == compiled_out.dtype:
+            print("✓ Output dtypes match between eager and XLA execution")
+        else:
+            print(f"✗ Dtype mismatch: eager {eager_out.dtype} vs XLA {compiled_out.dtype}")
+            
+    except Exception as e:
+        print(f"✗ Unexpected error in fixed version: {type(e).__name__}: {e}")
+        print("   This suggests an issue with the XLA environment or tf.cond implementation.")
+
+
+def check_xla_availability():
+    """Check if XLA/JIT compilation is available."""
+    try:
+        @tf.function(jit_compile=True)
+        def test_xla():
+            return tf.constant(1.0)
+        test_xla()
+        return True
+    except Exception:
+        return False
+
+
+def main():
+    """Run both test cases to demonstrate the issue and the fix."""
+    print("=" * 70)
+    print("GitHub Issue #105133: XLA Conditional Compatibility Demo")
+    print("OperatorNotAllowedInGraphError with tf.shape()[0] in conditionals")
+    print("=" * 70)
+    
+    # Check environment
+    xla_available = check_xla_availability()
+    print(f"TensorFlow version: {tf.__version__}")
+    print(f"XLA/JIT compilation available: {xla_available}")
+    
+    if not xla_available:
+        print("⚠️  WARNING: XLA/JIT compilation not available.")
+        print("   Some parts of this demo may not show the expected behavior.")
+        print("   Run on an XLA-enabled TensorFlow build for full demonstration.")
+    
+    print()
+    
+    # Test the problematic version (expected to fail with XLA)
+    test_problematic_version()
+    
+    # Test the fixed version (should succeed with XLA)  
+    test_fixed_version()
+    
+    print("\n" + "=" * 70)
+    print("SOLUTION SUMMARY:")
+    print("Issue:    Python 'if' with symbolic tensors fails under XLA compilation")  
+    print("Solution: Replace with tf.cond() for XLA compatibility")
+    print()
+    print("Key Refactoring Patterns:")
+    print("  • if condition:           -> tf.cond(condition, true_fn, false_fn)")
+    print("  • if a and b:            -> tf.cond(tf.logical_and(a, b), ...)")
+    print("  • if a == b:             -> tf.cond(tf.equal(a, b), ...)")
+    print("  • if tf.shape(x)[0] > n: -> tf.cond(tf.shape(x)[0] > n, ...)")
+    print()
+    print("See xla_conditional_compatibility_test.py for CI-safe unit tests.")
+    print("=" * 70)
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/tensorflow/compiler/tf2xla/kernels/xla_conditional_compatibility_test.py b/tensorflow/compiler/tf2xla/kernels/xla_conditional_compatibility_test.py
new file mode 100644
index 00000000000000..aa76d16e426154
--- /dev/null
+++ b/tensorflow/compiler/tf2xla/kernels/xla_conditional_compatibility_test.py
@@ -0,0 +1,410 @@
+"""CI-safe unit tests for XLA compatibility with conditional operations.
+
+Regression test for GitHub issue #105133:
+OperatorNotAllowedInGraphError when using tf.shape()[0] in conditional with jit_compile=True
+
+Tested with TF 2.20.0+ on CPU/GPU builds.
+XLA-specific tests are skipped when JIT compilation is unavailable.
+
+This demonstrates user workarounds (tf.cond) vs problematic patterns (Python if with symbolic tensors).
+Integration demo requiring XLA-enabled builds is in issue_105133_fix_demo.py.
+"""
+
+import tensorflow as tf
+import unittest
+import os
+
+
+
+def _is_xla_available():
+  """Check if XLA/JIT compilation is available in this build."""
+  try:
+    # Try to compile a simple function to test XLA availability
+    @tf.function(jit_compile=True)
+    def test_fn():
+      return tf.constant(1.0)
+    test_fn()
+    return True
+  except (tf.errors.UnimplementedError, tf.errors.InvalidArgumentError):
+    return False
+  except Exception:
+    return False
+
+
+class XlaConditionalCompatibilityTest(tf.test.TestCase):
+  """CI-safe unit tests for XLA conditional compatibility.
+  
+  Tests eager execution behavior and tf.cond workarounds.
+  XLA-specific tests are skipped when JIT is unavailable.
+  """
+
+  def test_eager_execution_with_python_conditionals(self):
+    """Verify that Python conditionals work correctly in eager execution."""
+    
+    class TestModel(tf.keras.Model):
+      def __init__(self):
+        super().__init__()
+        self.dense = tf.keras.layers.Dense(10)
+
+      def call(self, x):
+        # These work fine in eager execution
+        if tf.shape(x)[0] >= 1:
+          x = tf.stop_gradient(x)
+        
+        h, w = tf.shape(x)[1], tf.shape(x)[2]
+        if h > 1 and w > 1:
+          x = tf.nn.relu(x)
+        else:
+          x = tf.nn.tanh(x)
+        
+        x = tf.reshape(x, [tf.shape(x)[0], -1])
+        return self.dense(x)
+
+    model = TestModel()
+    x = tf.random.normal([4, 8, 8, 3])
+    
+    # Should work without errors in eager execution
+    result = model(x)
+    
+    # Verify correct shapes and behavior
+    self.assertEqual(result.shape[0], 4)  # Batch size preserved
+    self.assertEqual(result.shape[1], 10)  # Output dimension correct
+
+  @unittest.skipUnless(_is_xla_available(), "XLA/JIT compilation not available")
+  def test_python_if_with_symbolic_shape_raises_under_xla(self):
+    """Test that Python if with symbolic tensors fails under XLA compilation."""
+    
+    def problematic_function(x):
+      # This pattern causes OperatorNotAllowedInGraphError in XLA
+      if tf.shape(x)[0] >= 1:  # PROBLEMATIC: symbolic tensor as Python bool
+        return tf.stop_gradient(x)
+      return x
+
+    x = tf.random.normal([4, 8])
+    
+    # Should work in eager execution
+    eager_result = problematic_function(x)
+    self.assertEqual(eager_result.shape, x.shape)
+    
+    # Should fail when compiled with XLA
+    @tf.function(jit_compile=True)
+    def compiled_function(inputs):
+      return problematic_function(inputs)
+    
+    # Check for expected error message patterns
+    with self.assertRaises(Exception) as context:
+      compiled_function(x)
+    
+    error_msg = str(context.exception)
+    # Verify it's the expected symbolic tensor error
+    self.assertTrue(
+        any(keyword in error_msg.lower() for keyword in 
+            ["symbolic", "python bool", "not allowed", "operatornotallowed"]),
+        f"Expected symbolic tensor error, got: {error_msg}"
+    )
+
+  def test_tf_cond_replacement_allows_jit_compilation(self):
+    """Test that tf.cond replacement works in both eager and XLA modes."""
+    
+    def tf_cond_function(x):
+      # XLA-compatible version using tf.cond
+      return tf.cond(
+          tf.shape(x)[0] >= 1,
+          lambda: tf.stop_gradient(x),
+          lambda: x
+      )
+
+    x = tf.random.normal([4, 8])
+    
+    # Test eager execution
+    eager_result = tf_cond_function(x)
+    self.assertEqual(eager_result.shape, x.shape)
+    
+    # Test graph mode (without XLA first)
+    @tf.function
+    def graph_function(inputs):
+      return tf_cond_function(inputs)
+    
+    graph_result = graph_function(x)
+    self.assertEqual(graph_result.shape, x.shape)
+    
+    # Test deterministic behavior: stop_gradient should preserve values
+    self.assertAllClose(eager_result, x)
+    self.assertAllClose(graph_result, x)
+
+  @unittest.skipUnless(_is_xla_available(), "XLA/JIT compilation not available")  
+  def test_tf_cond_works_under_xla_compilation(self):
+    """Test that tf.cond works correctly under XLA compilation."""
+    
+    def xla_compatible_conditional(x):
+      return tf.cond(
+          tf.shape(x)[0] > 2,
+          lambda: tf.nn.relu(x),  # Non-negative for large batch
+          lambda: tf.nn.tanh(x)   # Can be negative for small batch
+      )
+
+    # Test with different batch sizes to verify conditional logic
+    x_small = tf.random.normal([2, 4])  # batch_size <= 2, should use tanh
+    x_large = tf.random.normal([5, 4])  # batch_size > 2, should use relu
+    
+    @tf.function(jit_compile=True)
+    def compiled_function(inputs):
+      return xla_compatible_conditional(inputs)
+    
+    # Should compile and run without errors
+    result_small = compiled_function(x_small)
+    result_large = compiled_function(x_large)
+    
+    # Verify correct conditional behavior
+    self.assertEqual(result_small.shape, x_small.shape)
+    self.assertEqual(result_large.shape, x_large.shape)
+    
+    # relu output should be non-negative, tanh can be negative
+    self.assertTrue(tf.reduce_all(result_large >= 0).numpy(), 
+                   "relu output should be non-negative")
+    # Note: We can't guarantee tanh produces negative values with random input,
+    # but we can check it's different from relu behavior
+    eager_small = xla_compatible_conditional(x_small)
+    self.assertAllClose(result_small, eager_small, rtol=1e-5)
+
+  def test_tf_where_alternative_for_simple_conditionals(self):
+    """Test tf.where as an alternative to Python conditionals."""
+    
+    def where_based_function(x):
+      # Use tf.where for element-wise conditionals
+      return tf.where(
+          tf.reduce_sum(x, axis=-1, keepdims=True) > 0,
+          tf.stop_gradient(x),
+          x * 0.5
+      )
+
+    x = tf.random.normal([4, 8])
+    
+    # Test eager execution
+    eager_result = where_based_function(x)
+    self.assertEqual(eager_result.shape, x.shape)
+    
+    # Test graph mode
+    @tf.function
+    def graph_function(inputs):
+      return where_based_function(inputs)
+    
+    graph_result = graph_function(x)
+    self.assertEqual(graph_result.shape, x.shape)
+    self.assertAllClose(eager_result, graph_result)
+
+  def test_mathematical_masking_alternative(self):
+    """Test mathematical operations as alternative to conditionals."""
+    
+    def mask_based_function(x):
+      # Use mathematical operations to avoid explicit conditionals
+      batch_size = tf.cast(tf.shape(x)[0], tf.float32)
+      threshold = tf.constant(3.0)
+      
+      # Create mask: 1.0 if batch_size > threshold, 0.0 otherwise
+      mask = tf.cast(batch_size > threshold, tf.float32)
+      
+      # Apply different operations based on mask
+      large_batch_result = tf.nn.l2_normalize(x, axis=-1)
+      small_batch_result = x * 0.5
+      
+      # Combine results using the mask
+      return mask * large_batch_result + (1.0 - mask) * small_batch_result
+
+    # Test with different batch sizes
+    x_small = tf.random.normal([2, 4])  # batch_size <= 3
+    x_large = tf.random.normal([5, 4])  # batch_size > 3
+    
+    result_small = mask_based_function(x_small)
+    result_large = mask_based_function(x_large)
+    
+    # Verify shapes
+    self.assertEqual(result_small.shape, x_small.shape)
+    self.assertEqual(result_large.shape, x_large.shape)
+    
+    # Verify behavior: large batch should be normalized (norm ≈ 1)
+    norms_large = tf.norm(result_large, axis=-1)
+    self.assertAllClose(norms_large, tf.ones_like(norms_large), atol=1e-5)
+    
+    # Small batch should be scaled by 0.5
+    expected_small = x_small * 0.5
+    self.assertAllClose(result_small, expected_small)
+
+  def test_safe_shape_operations_in_tensorflow_ops(self):
+    """Test that tf.shape operations are safe when used in TF ops (not Python conditionals)."""
+    
+    def safe_shape_operations(x):
+      # These are safe: tf.shape used in TensorFlow operations, not Python conditionals
+      current_shape = tf.shape(x)
+      batch_size = current_shape[0]
+      
+      # Safe: Using shape values in TensorFlow operations
+      half_batch = batch_size // 2
+      
+      # Safe: Reshape using computed shapes
+      new_shape = tf.concat([current_shape[:1], [-1]], axis=0)
+      reshaped = tf.reshape(x, new_shape)
+      
+      # Safe: Gather operations with dynamic indices  
+      indices = tf.range(half_batch)
+      gathered = tf.gather(reshaped, indices)
+      
+      return gathered, batch_size, half_batch
+
+    x = tf.random.normal([10, 8, 4])
+    
+    # Test eager execution
+    result, batch_size, half_batch = safe_shape_operations(x)
+    
+    # Verify correct behavior
+    self.assertEqual(len(result.shape), 2)
+    self.assertEqual(batch_size.numpy(), 10)
+    self.assertEqual(half_batch.numpy(), 5)
+    self.assertEqual(result.shape[0], 5)  # Half the batch size
+    
+    # Test graph mode
+    @tf.function
+    def graph_function(inputs):
+      return safe_shape_operations(inputs)
+    
+    graph_result, graph_batch, graph_half = graph_function(x)
+    
+    # Verify consistency between eager and graph modes
+    self.assertAllClose(result, graph_result)
+    self.assertEqual(batch_size.numpy(), graph_batch.numpy())
+    self.assertEqual(half_batch.numpy(), graph_half.numpy())
+
+  @unittest.skipUnless(_is_xla_available(), "XLA/JIT compilation not available")
+  def test_safe_shape_operations_under_xla(self):
+    """Test that safe shape operations work under XLA compilation."""
+    
+    @tf.function(jit_compile=True)
+    def xla_safe_operations(x):
+      # These operations should work fine in XLA
+      batch_size = tf.shape(x)[0]
+      feature_dim = tf.shape(x)[-1]
+      
+      # Use shapes in TensorFlow operations (not Python conditionals)
+      half_batch = batch_size // 2
+      new_shape = [half_batch, feature_dim * 2]
+      
+      # Reshape and return
+      x_subset = x[:half_batch]
+      x_doubled = tf.concat([x_subset, x_subset], axis=-1)
+      return tf.reshape(x_doubled, new_shape)
+
+    x = tf.random.normal([8, 4])
+    result = xla_safe_operations(x)
+    
+    # Verify expected output shape
+    self.assertEqual(result.shape[0], 4)  # half_batch
+    self.assertEqual(result.shape[1], 8)  # feature_dim * 2
+
+
+class XlaConditionalBestPracticesTest(tf.test.TestCase):
+  """Best practices for writing XLA-compatible conditional code."""
+
+  def test_refactoring_patterns_eager_execution(self):
+    """Test refactoring patterns work correctly in eager execution."""
+    
+    def tf_cond_pattern(x):
+      # XLA-compatible version using tf.cond
+      return tf.cond(
+          tf.shape(x)[0] > 3,
+          true_fn=lambda: tf.nn.relu(x),
+          false_fn=lambda: tf.nn.tanh(x)
+      )
+    
+    # Test with different batch sizes
+    x_small = tf.random.normal([2, 4])  # batch_size <= 3, should use tanh  
+    x_large = tf.random.normal([5, 4])  # batch_size > 3, should use relu
+    
+    result_small = tf_cond_pattern(x_small)
+    result_large = tf_cond_pattern(x_large)
+    
+    # Verify shapes are preserved
+    self.assertEqual(result_small.shape, x_small.shape)
+    self.assertEqual(result_large.shape, x_large.shape)
+    
+    # Verify relu produces non-negative output
+    self.assertTrue(tf.reduce_all(result_large >= 0).numpy())
+
+  @unittest.skipUnless(_is_xla_available(), "XLA/JIT compilation not available")
+  def test_refactoring_patterns_under_xla(self):
+    """Test refactoring patterns work under XLA compilation."""
+    
+    @tf.function(jit_compile=True)
+    def xla_conditional_pattern(x):
+      return tf.cond(
+          tf.shape(x)[0] > 3,
+          true_fn=lambda: tf.nn.relu(x),
+          false_fn=lambda: tf.nn.tanh(x)
+      )
+    
+    # Test compilation and execution
+    x_small = tf.random.normal([2, 4])
+    x_large = tf.random.normal([5, 4]) 
+    
+    # Should compile and run without errors
+    result_small = xla_conditional_pattern(x_small)
+    result_large = xla_conditional_pattern(x_large)
+    
+    # Verify correct behavior
+    self.assertEqual(result_small.shape, x_small.shape)
+    self.assertEqual(result_large.shape, x_large.shape)
+    self.assertTrue(tf.reduce_all(result_large >= 0).numpy())
+
+  def test_nested_tf_cond_patterns(self):
+    """Test nested tf.cond patterns work in eager and graph modes."""
+    
+    def nested_conditional_function(x, training=True):
+      """Complex conditional logic using nested tf.cond operations."""
+      
+      batch_size = tf.shape(x)[0]
+      feature_dim = tf.shape(x)[-1]
+      
+      def training_path():
+        return tf.cond(
+            batch_size > 8,
+            lambda: tf.nn.dropout(x, rate=0.3),  # Large batch: dropout
+            lambda: x  # Small batch: no dropout
+        )
+      
+      def inference_path():
+        return tf.cond(
+            feature_dim > 16,
+            lambda: tf.nn.l2_normalize(x, axis=-1),  # High-dim: normalize  
+            lambda: x * 0.5  # Low-dim: scale down
+        )
+      
+      # Top-level condition
+      return tf.cond(
+          training,
+          training_path,
+          inference_path
+      )
+
+    x = tf.random.normal([10, 32])
+    
+    # Test training mode  
+    train_result = nested_conditional_function(x, training=True)
+    self.assertEqual(train_result.shape, x.shape)
+    
+    # Test inference mode
+    inference_result = nested_conditional_function(x, training=False)  
+    self.assertEqual(inference_result.shape, x.shape)
+    
+    # For high-dim inference, should be normalized (feature_dim=32 > 16)
+    norms = tf.norm(inference_result, axis=-1)
+    self.assertAllClose(norms, tf.ones_like(norms), atol=1e-5)
+
+
+if __name__ == '__main__':
+  # Set up test environment
+  tf.config.run_functions_eagerly(False)
+  
+  # Print XLA availability for debugging
+  print(f"XLA/JIT compilation available: {_is_xla_available()}")
+  
+  # Run the tests
+  tf.test.main()
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