text: # Copyright 2018 The TensorFlow Probability Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ============================================================================
  """Experimental Numpy backend."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  
  # pylint: disable=unused-import
  from tensorflow_probability.python.internal.backend.numpy import _utils as utils
  from tensorflow_probability.python.internal.backend.numpy import bitwise
  from tensorflow_probability.python.internal.backend.numpy import debugging
  from tensorflow_probability.python.internal.backend.numpy import errors
  from tensorflow_probability.python.internal.backend.numpy import keras
  from tensorflow_probability.python.internal.backend.numpy import linalg
  from tensorflow_probability.python.internal.backend.numpy import nn
  from tensorflow_probability.python.internal.backend.numpy import numpy_logging as logging
  from tensorflow_probability.python.internal.backend.numpy import numpy_math as math
  from tensorflow_probability.python.internal.backend.numpy import numpy_signal as signal
  from tensorflow_probability.python.internal.backend.numpy import random_generators as random
  from tensorflow_probability.python.internal.backend.numpy import raw_ops
  from tensorflow_probability.python.internal.backend.numpy import sets_lib as sets
  from tensorflow_probability.python.internal.backend.numpy import sparse_lib as sparse
  from tensorflow_probability.python.internal.backend.numpy import test_lib as test
  from tensorflow_probability.python.internal.backend.numpy.control_flow import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.dtype import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.functional_ops import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.misc import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.numpy_array import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.numpy_math import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.ops import *  # pylint: disable=wildcard-import
  from tensorflow_probability.python.internal.backend.numpy.tensor_array_ops import TensorArray
  from tensorflow_probability.python.internal.backend.numpy.tensor_shape import TensorShape
  # TODO(b/151669121): Remove dependency on TF
  from tensorflow.python.util import nest  # pylint: disable=g-direct-tensorflow-import
  # pylint: enable=unused-import
  
  
  JAX_MODE = False
  
  
  def _function(func=None, input_signature=None, autograph=True,  # pylint: disable=unused-argument
                experimental_autograph_options=None,  # pylint: disable=unused-argument
                experimental_relax_shapes=False, experimental_compile=None):  # pylint: disable=unused-argument
    """Dummy version of `tf.function`."""
    transform = lambda fn: fn
    if experimental_compile:
      if JAX_MODE:
        from jax import jit  # pylint: disable=g-import-not-at-top
        transform = jit
      else:
        raise NotImplementedError('Could not find compiler: Numpy only.')
    # This code path is for the `foo = tf.function(foo, ...)` use case.
    if func is not None:
      return transform(func)
    # This code path is for the following use case:
    #   @tf.function(...)
    #   def foo(...):
    #      ...
    # This case is equivalent to `foo = tf.function(...)(foo)`.
    return transform
  
  
  # --- Begin Public Functions --------------------------------------------------
  
  
  compat = collections.namedtuple('compat', 'dimension_value')(
      lambda dim: None if dim is None else int(dim))
  
  function = utils.copy_docstring(
      'tf.function',
      _function)
  
  eye = linalg.eye
  matmul = linalg.matmul
  
  del collections, utils
  