[tensorflow/tensorflow] XLA Compilation Fails Due to Data Type Mismatch in tf.pow Operation with Mixed Integer and Float Arguments

### ROOT CAUSE The `tf.pow` operation in TensorFlow requires both arguments to be of the same numeric type. When an integer tensor and a float tensor are used together, TensorFlow attempts to implicitly convert the integer to float, but XLA's compiler encounters a type mismatch during graph optimization, leading to a compilation failure. This occurs because XLA expects explicit type annotations for operations involving mixed numeric types. ### CODE FIX Explicitly cast the integer tensor to float before applying `tf.pow` to ensure type compatibility during XLA compilation. ```python import tensorflow as tf # Original problematic code # result = tf.pow(tf.constant(2, dtype=tf.int32), tf.constant(0.5, dtype=tf.float32)) # Fixed code result = tf.pow(tf.cast(tf.constant(2, dtype=tf.int32), tf.float32), tf.constant(0.5, dtype=tf.float32)) ```