code: def shape(x):
      """Always return a tuple shape.
  
      `tf.shape` will return a `tf.Tensor`, which differs from the tuple return
      type on the torch and jax backends. We write our own method instead which
      always returns a tuple, with integer values when the shape is known, and
      tensor values when the shape is unknown (this is tf specific, as dynamic
      shapes do not apply in other backends).
      """
      if not tf.is_tensor(x):
          x = tf.convert_to_tensor(x)
      dynamic = tf.shape(x)
      if x.shape == tf.TensorShape(None):
          raise ValueError(
              "All tensors passed to `ops.shape` must have a statically known "
              f"rank. Received: x={x} with unknown rank."
          )
      static = x.shape.as_list()
      return tuple(
          dynamic[i] if s is None else s for i, s in enumerate(static)
      )
  
imports: import types
  import numpy
  import tensorflow
  from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
  from keras.backend.common import KerasVariable
  from keras.backend.common import global_state
  from keras.backend.common import standardize_dtype
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.name_scope import name_scope
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.common.stateless_scope import in_stateless_scope
  from keras.utils.naming import auto_name
label_desc: Use image node_id 4 for calling a global function with example usage: shape(x) and returns: tuple