code: def same_dynamic_shape(a, b):
      """Returns whether a and b have the same dynamic shape.
  
      Args:
        a: `Tensor`
        b: `Tensor`
  
      Returns:
        `bool` `Tensor` representing if both tensors have the same shape.
      """
      a = tf.convert_to_tensor(a, name="a")
      b = tf.convert_to_tensor(b, name="b")
  
      # Here we can't just do tf.equal(a.shape, b.shape), since
      # static shape inference may break the equality comparison between
      # shape(a) and shape(b) in tf.equal.
      def all_shapes_equal():
          return tf.reduce_all(
              tf.equal(
                  tf.concat([tf.shape(a), tf.shape(b)], 0),
                  tf.concat([tf.shape(b), tf.shape(a)], 0),
              )
          )
  
      # One of the shapes isn't fully defined, so we need to use the dynamic
      # shape.
      return tf.cond(
          pred=tf.equal(tf.rank(a), tf.rank(b)),
          true_fn=all_shapes_equal,
          false_fn=lambda: tf.constant(False),
      )
  
imports: import functools
  import hashlib
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 19 for calling a global function with example usage: same_dynamic_shape(a, b) and returns: tf, tf