def same_dynamic_shape(a, b):
a = tf.convert_to_tensor(value=a, name="<STR_LIT:a>")<EOL>b = tf.convert_to_tensor(value=b, name="<STR_LIT:b>")<EOL>def all_shapes_equal():<EOL><INDENT>return tf.reduce_all(<EOL>input_tensor=tf.equal(<EOL>tf.concat([tf.shape(input=a), tf.shape(input=b)], <NUM_LIT:0>),<EOL>tf.concat([tf.shape(input=b), tf.shape(input=a)], <NUM_LIT:0>)))<EOL><DEDENT>return tf.cond(<EOL>pred=tf.equal(tf.rank(a), tf.rank(b)),<EOL>true_fn=all_shapes_equal,<EOL>false_fn=lambda: tf.constant(False))<EOL>
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.