Fork the tf_inspect to keras, which use tf_decorator.unwrap as __internal__ API.

PiperOrigin-RevId: 331660689
Change-Id: Ib7310b991986f71e0d7c2e9a53d7427e62db2d69

tensorflow/python/keras/BUILD

@@ -95,6 +95,7 @@ py_library(
         "//tensorflow/python/distribute:multi_worker_util",
         "//tensorflow/python/keras/engine:keras_tensor",
         "//tensorflow/python/keras/utils:control_flow_util",
+        "//tensorflow/python/keras/utils:tf_inspect",
     ],
 )
 

tensorflow/python/keras/backend.py

@@ -55,6 +55,7 @@ from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend_config
 from tensorflow.python.keras.engine import keras_tensor
 from tensorflow.python.keras.utils import control_flow_util
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import clip_ops
 from tensorflow.python.ops import control_flow_ops
@@ -83,7 +84,6 @@ from tensorflow.python.util import dispatch
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
 from tensorflow.python.util import tf_contextlib
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 from tensorflow.tools.docs import doc_controls
 

tensorflow/python/keras/backend_test.py

@@ -36,11 +36,11 @@ from tensorflow.python.keras import combinations
 from tensorflow.python.keras.engine import input_layer
 from tensorflow.python.keras.layers import advanced_activations
 from tensorflow.python.keras.layers import normalization
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import nn
 from tensorflow.python.ops import variables
 from tensorflow.python.platform import test
-from tensorflow.python.util import tf_inspect
 
 
 def compare_single_input_op_to_numpy(keras_op,

tensorflow/python/keras/benchmarks/eager_microbenchmarks_test.py

@@ -23,8 +23,8 @@ import six
 import tensorflow as tf
 
 from tensorflow.python.eager import context
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.platform import benchmark
-from tensorflow.python.util import tf_inspect
 
 
 def _run_benchmark(func, num_iters, execution_mode=None):

tensorflow/python/keras/engine/BUILD

@@ -102,6 +102,7 @@ py_library(
         "//tensorflow/python/distribute:distribute_lib",
         "//tensorflow/python/eager:context",
         "//tensorflow/python/keras:backend",
+        "//tensorflow/python/keras/utils:tf_inspect",
         "//tensorflow/python/keras/utils:tf_utils",
     ],
 )

tensorflow/python/keras/engine/base_layer.py

@@ -62,11 +62,13 @@ from tensorflow.python.keras.mixed_precision.experimental import policy
 from tensorflow.python.keras.saving.saved_model import layer_serialization
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.keras.utils import version_utils
 # A module that only depends on `keras.layers` import these from here.
 from tensorflow.python.keras.utils.generic_utils import to_snake_case  # pylint: disable=unused-import
 from tensorflow.python.keras.utils.tf_utils import is_tensor_or_tensor_list  # pylint: disable=unused-import
+
 from tensorflow.python.module import module
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
@@ -82,7 +84,6 @@ from tensorflow.python.training.tracking import tracking
 from tensorflow.python.util import compat
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 from tensorflow.tools.docs import doc_controls
 

tensorflow/python/keras/engine/base_layer_utils.py

@@ -30,6 +30,7 @@ from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.utils import control_flow_util
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_util_v2
@@ -37,7 +38,6 @@ from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.training.tracking import base as tracking
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 _call_context = threading.local()

tensorflow/python/keras/engine/base_layer_v1.py

@@ -52,6 +52,7 @@ from tensorflow.python.keras.mixed_precision.experimental import policy
 from tensorflow.python.keras.saving.saved_model import layer_serialization
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 # A module that only depends on `keras.layers` import these from here.
 from tensorflow.python.keras.utils.generic_utils import to_snake_case  # pylint: disable=unused-import
@@ -69,7 +70,6 @@ from tensorflow.python.training.tracking import layer_utils as trackable_layer_u
 from tensorflow.python.training.tracking import tracking
 from tensorflow.python.util import nest
 from tensorflow.python.util import object_identity
-from tensorflow.python.util import tf_inspect
 from tensorflow.tools.docs import doc_controls
 
 

tensorflow/python/keras/engine/functional.py

@@ -40,13 +40,13 @@ from tensorflow.python.keras.engine import training as training_lib
 from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.saving.saved_model import network_serialization
 from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 
 
 # pylint: disable=g-classes-have-attributes

tensorflow/python/keras/engine/sequential.py

@@ -33,12 +33,12 @@ from tensorflow.python.keras.engine import training_utils
 from tensorflow.python.keras.saving.saved_model import model_serialization
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops.numpy_ops import np_arrays
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 

tensorflow/python/keras/engine/training_utils.py

@@ -51,13 +51,13 @@ from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import losses_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.compat import collections_abc
 
 

tensorflow/python/keras/engine/training_v1.py

@@ -55,6 +55,7 @@ from tensorflow.python.keras.optimizer_v2 import optimizer_v2
 from tensorflow.python.keras.saving.saved_model import model_serialization
 from tensorflow.python.keras.utils import data_utils
 from tensorflow.python.keras.utils import losses_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils.mode_keys import ModeKeys
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
@@ -63,7 +64,6 @@ from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
 from tensorflow.python.types import core
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.compat import collections_abc
 
 try:

tensorflow/python/keras/initializers/__init__.py

@@ -25,8 +25,8 @@ from tensorflow.python import tf2
 from tensorflow.python.keras.initializers import initializers_v1
 from tensorflow.python.keras.initializers import initializers_v2
 from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.keras.utils import tf_inspect as inspect
 from tensorflow.python.ops import init_ops
-from tensorflow.python.util import tf_inspect as inspect
 from tensorflow.python.util.tf_export import keras_export
 
 

tensorflow/python/keras/layers/core.py

@@ -46,6 +46,7 @@ from tensorflow.python.keras.layers.ops import core as core_ops
 from tensorflow.python.keras.utils import control_flow_util
 from tensorflow.python.keras.utils import conv_utils
 from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import gen_array_ops
@@ -57,7 +58,6 @@ from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import dispatch
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_decorator
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import get_canonical_name_for_symbol
 from tensorflow.python.util.tf_export import get_symbol_from_name
 from tensorflow.python.util.tf_export import keras_export

tensorflow/python/keras/layers/rnn_cell_wrapper_v2.py

@@ -26,8 +26,8 @@ from __future__ import print_function
 
 
 from tensorflow.python.keras.layers import recurrent
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import rnn_cell_wrapper_impl
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import tf_export
 
 

tensorflow/python/keras/layers/serialization.py

@@ -61,7 +61,7 @@ from tensorflow.python.keras.layers.preprocessing import string_lookup_v1 as pre
 from tensorflow.python.keras.layers.preprocessing import text_vectorization as preprocessing_text_vectorization
 from tensorflow.python.keras.layers.preprocessing import text_vectorization_v1 as preprocessing_text_vectorization_v1
 from tensorflow.python.keras.utils import generic_utils
-from tensorflow.python.util import tf_inspect as inspect
+from tensorflow.python.keras.utils import tf_inspect as inspect
 from tensorflow.python.util.tf_export import keras_export
 
 

tensorflow/python/keras/layers/wrappers.py

@@ -29,11 +29,11 @@ from tensorflow.python.keras.engine.input_spec import InputSpec
 from tensorflow.python.keras.layers.recurrent import _standardize_args
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops.ragged import ragged_tensor
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 

tensorflow/python/keras/metrics.py

@@ -56,6 +56,7 @@ from tensorflow.python.keras.losses import squared_hinge
 from tensorflow.python.keras.saving.saved_model import metric_serialization
 from tensorflow.python.keras.utils import losses_utils
 from tensorflow.python.keras.utils import metrics_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
 from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
 from tensorflow.python.keras.utils.generic_utils import to_list
@@ -72,7 +73,6 @@ from tensorflow.python.ops import weights_broadcast_ops
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import dispatch
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 from tensorflow.tools.docs import doc_controls
 

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

@@ -40,6 +40,7 @@ from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule
 from tensorflow.python.keras.optimizer_v2 import utils as optimizer_utils
 from tensorflow.python.keras.utils import generic_utils
 from tensorflow.python.keras.utils import layer_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import tf_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import control_flow_ops
@@ -50,7 +51,6 @@ from tensorflow.python.ops import variables as tf_variables
 from tensorflow.python.saved_model import revived_types
 from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.util import nest
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 

tensorflow/python/keras/preprocessing/image.py

@@ -33,11 +33,11 @@ from tensorflow.python.framework import ops
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.preprocessing.image_dataset import image_dataset_from_directory  # pylint: disable=unused-import
 from tensorflow.python.keras.utils import data_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import image_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import tf_logging
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 random_rotation = image.random_rotation

tensorflow/python/keras/saving/saved_model/save_impl.py

@@ -37,6 +37,7 @@ from tensorflow.python.keras.saving.saved_model import constants
 from tensorflow.python.keras.saving.saved_model import load as keras_load
 from tensorflow.python.keras.saving.saved_model import serialized_attributes
 from tensorflow.python.keras.saving.saved_model import utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils import version_utils
 from tensorflow.python.keras.utils.generic_utils import LazyLoader
 from tensorflow.python.platform import tf_logging as logging
@@ -44,7 +45,6 @@ from tensorflow.python.training.tracking import base as trackable
 from tensorflow.python.training.tracking import data_structures
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_decorator
-from tensorflow.python.util import tf_inspect
 
 # To avoid circular dependencies between keras/engine and keras/saving,
 # code in keras/saving must delay imports.

tensorflow/python/keras/saving/saved_model/saved_model_test.py

@@ -53,6 +53,7 @@ from tensorflow.python.keras.saving.saved_model import load as keras_load
 from tensorflow.python.keras.saving.saved_model import save_impl as keras_save
 from tensorflow.python.keras.utils import control_flow_util
 from tensorflow.python.keras.utils import generic_utils
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import init_ops
 from tensorflow.python.ops import math_ops
@@ -63,7 +64,6 @@ from tensorflow.python.platform import test
 from tensorflow.python.saved_model import load as tf_load
 from tensorflow.python.saved_model import save as tf_save
 from tensorflow.python.util import tf_contextlib
-from tensorflow.python.util import tf_inspect
 
 
 class LayerWithLearningPhase(keras.engine.base_layer.Layer):

tensorflow/python/keras/saving/saved_model/utils.py

@@ -24,10 +24,10 @@ from tensorflow.python.eager import context
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras.engine import base_layer_utils
 from tensorflow.python.keras.utils import control_flow_util
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils.generic_utils import LazyLoader
 from tensorflow.python.training.tracking import layer_utils as trackable_layer_utils
 from tensorflow.python.util import tf_decorator
-from tensorflow.python.util import tf_inspect
 
 
 # pylint:disable=g-inconsistent-quotes

tensorflow/python/keras/testing_utils.py

@@ -46,9 +46,9 @@ from tensorflow.python.keras.optimizer_v2 import adamax as adamax_v2
 from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
 from tensorflow.python.keras.optimizer_v2 import nadam as nadam_v2
 from tensorflow.python.keras.optimizer_v2 import rmsprop as rmsprop_v2
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util import tf_decorator
-from tensorflow.python.util import tf_inspect
 
 
 def string_test(actual, expected):

tensorflow/python/keras/utils/BUILD

@@ -197,6 +197,15 @@ py_library(
     ],
 )
 
+py_library(
+    name = "tf_inspect",
+    srcs = ["tf_inspect.py"],
+    srcs_version = "PY2AND3",
+    deps = [
+        "//tensorflow/python:util",
+    ],
+)
+
 py_library(
     name = "vis_utils",
     srcs = [

tensorflow/python/keras/utils/data_utils.py

@@ -45,11 +45,11 @@ from six.moves.urllib.error import URLError
 
 from tensorflow.python.framework import ops
 from six.moves.urllib.request import urlopen
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.keras.utils.generic_utils import Progbar
 from tensorflow.python.keras.utils.io_utils import path_to_string
 from tensorflow.python.platform import tf_logging as logging
 from tensorflow.python.util import deprecation
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 

tensorflow/python/keras/utils/generic_utils.py

@@ -29,11 +29,10 @@ import types as python_types
 
 import numpy as np
 import six
-
+from tensorflow.python.keras.utils import tf_inspect
 from tensorflow.python.util import nest
 from tensorflow.python.util import tf_contextlib
 from tensorflow.python.util import tf_decorator
-from tensorflow.python.util import tf_inspect
 from tensorflow.python.util.tf_export import keras_export
 
 _GLOBAL_CUSTOM_OBJECTS = {}

tensorflow/python/keras/utils/tf_inspect.py

@@ -0,0 +1,402 @@
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""TFDecorator-aware replacements for the inspect module."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import functools
+import inspect as _inspect
+
+import six
+
+from tensorflow.python.util import tf_decorator
+
+ArgSpec = _inspect.ArgSpec
+
+
+if hasattr(_inspect, 'FullArgSpec'):
+  FullArgSpec = _inspect.FullArgSpec  # pylint: disable=invalid-name
+else:
+  FullArgSpec = collections.namedtuple('FullArgSpec', [
+      'args', 'varargs', 'varkw', 'defaults', 'kwonlyargs', 'kwonlydefaults',
+      'annotations'
+  ])
+
+
+def _convert_maybe_argspec_to_fullargspec(argspec):
+  if isinstance(argspec, FullArgSpec):
+    return argspec
+  return FullArgSpec(
+      args=argspec.args,
+      varargs=argspec.varargs,
+      varkw=argspec.keywords,
+      defaults=argspec.defaults,
+      kwonlyargs=[],
+      kwonlydefaults=None,
+      annotations={})
+
+if hasattr(_inspect, 'getfullargspec'):
+  _getfullargspec = _inspect.getfullargspec  # pylint: disable=invalid-name
+
+  def _getargspec(target):
+    """A python3 version of getargspec.
+
+    Calls `getfullargspec` and assigns args, varargs,
+    varkw, and defaults to a python 2/3 compatible `ArgSpec`.
+
+    The parameter name 'varkw' is changed to 'keywords' to fit the
+    `ArgSpec` struct.
+
+    Args:
+      target: the target object to inspect.
+
+    Returns:
+      An ArgSpec with args, varargs, keywords, and defaults parameters
+      from FullArgSpec.
+    """
+    fullargspecs = getfullargspec(target)
+    argspecs = ArgSpec(
+        args=fullargspecs.args,
+        varargs=fullargspecs.varargs,
+        keywords=fullargspecs.varkw,
+        defaults=fullargspecs.defaults)
+    return argspecs
+else:
+  _getargspec = _inspect.getargspec
+
+  def _getfullargspec(target):
+    """A python2 version of getfullargspec.
+
+    Args:
+      target: the target object to inspect.
+
+    Returns:
+      A FullArgSpec with empty kwonlyargs, kwonlydefaults and annotations.
+    """
+    return _convert_maybe_argspec_to_fullargspec(getargspec(target))
+
+
+def getargspec(obj):
+  """TFDecorator-aware replacement for `inspect.getargspec`.
+
+  Note: `getfullargspec` is recommended as the python 2/3 compatible
+  replacement for this function.
+
+  Args:
+    obj: A function, partial function, or callable object, possibly decorated.
+
+  Returns:
+    The `ArgSpec` that describes the signature of the outermost decorator that
+    changes the callable's signature, or the `ArgSpec` that describes
+    the object if not decorated.
+
+  Raises:
+    ValueError: When callable's signature can not be expressed with
+      ArgSpec.
+    TypeError: For objects of unsupported types.
+  """
+  if isinstance(obj, functools.partial):
+    return _get_argspec_for_partial(obj)
+
+  decorators, target = tf_decorator.unwrap(obj)
+
+  spec = next((d.decorator_argspec
+               for d in decorators
+               if d.decorator_argspec is not None), None)
+  if spec:
+    return spec
+
+  try:
+    # Python3 will handle most callables here (not partial).
+    return _getargspec(target)
+  except TypeError:
+    pass
+
+  if isinstance(target, type):
+    try:
+      return _getargspec(target.__init__)
+    except TypeError:
+      pass
+
+    try:
+      return _getargspec(target.__new__)
+    except TypeError:
+      pass
+
+  # The `type(target)` ensures that if a class is received we don't return
+  # the signature of its __call__ method.
+  return _getargspec(type(target).__call__)
+
+
+def _get_argspec_for_partial(obj):
+  """Implements `getargspec` for `functools.partial` objects.
+
+  Args:
+    obj: The `functools.partial` object
+  Returns:
+    An `inspect.ArgSpec`
+  Raises:
+    ValueError: When callable's signature can not be expressed with
+      ArgSpec.
+  """
+  # When callable is a functools.partial object, we construct its ArgSpec with
+  # following strategy:
+  # - If callable partial contains default value for positional arguments (ie.
+  # object.args), then final ArgSpec doesn't contain those positional arguments.
+  # - If callable partial contains default value for keyword arguments (ie.
+  # object.keywords), then we merge them with wrapped target. Default values
+  # from callable partial takes precedence over those from wrapped target.
+  #
+  # However, there is a case where it is impossible to construct a valid
+  # ArgSpec. Python requires arguments that have no default values must be
+  # defined before those with default values. ArgSpec structure is only valid
+  # when this presumption holds true because default values are expressed as a
+  # tuple of values without keywords and they are always assumed to belong to
+  # last K arguments where K is number of default values present.
+  #
+  # Since functools.partial can give default value to any argument, this
+  # presumption may no longer hold in some cases. For example:
+  #
+  # def func(m, n):
+  #   return 2 * m + n
+  # partialed = functools.partial(func, m=1)
+  #
+  # This example will result in m having a default value but n doesn't. This is
+  # usually not allowed in Python and can not be expressed in ArgSpec correctly.
+  #
+  # Thus, we must detect cases like this by finding first argument with default
+  # value and ensures all following arguments also have default values. When
+  # this is not true, a ValueError is raised.
+
+  n_prune_args = len(obj.args)
+  partial_keywords = obj.keywords or {}
+
+  args, varargs, keywords, defaults = getargspec(obj.func)
+
+  # Pruning first n_prune_args arguments.
+  args = args[n_prune_args:]
+
+  # Partial function may give default value to any argument, therefore length
+  # of default value list must be len(args) to allow each argument to
+  # potentially be given a default value.
+  no_default = object()
+  all_defaults = [no_default] * len(args)
+
+  if defaults:
+    all_defaults[-len(defaults):] = defaults
+
+  # Fill in default values provided by partial function in all_defaults.
+  for kw, default in six.iteritems(partial_keywords):
+    if kw in args:
+      idx = args.index(kw)
+      all_defaults[idx] = default
+    elif not keywords:
+      raise ValueError('Function does not have **kwargs parameter, but '
+                       'contains an unknown partial keyword.')
+
+  # Find first argument with default value set.
+  first_default = next(
+      (idx for idx, x in enumerate(all_defaults) if x is not no_default), None)
+
+  # If no default values are found, return ArgSpec with defaults=None.
+  if first_default is None:
+    return ArgSpec(args, varargs, keywords, None)
+
+  # Checks if all arguments have default value set after first one.
+  invalid_default_values = [
+      args[i] for i, j in enumerate(all_defaults)
+      if j is no_default and i > first_default
+  ]
+
+  if invalid_default_values:
+    raise ValueError('Some arguments %s do not have default value, but they '
+                     'are positioned after those with default values. This can '
+                     'not be expressed with ArgSpec.' % invalid_default_values)
+
+  return ArgSpec(args, varargs, keywords, tuple(all_defaults[first_default:]))
+
+
+def getfullargspec(obj):
+  """TFDecorator-aware replacement for `inspect.getfullargspec`.
+
+  This wrapper emulates `inspect.getfullargspec` in[^)]* Python2.
+
+  Args:
+    obj: A callable, possibly decorated.
+
+  Returns:
+    The `FullArgSpec` that describes the signature of
+    the outermost decorator that changes the callable's signature. If the
+    callable is not decorated, `inspect.getfullargspec()` will be called
+    directly on the callable.
+  """
+  decorators, target = tf_decorator.unwrap(obj)
+
+  for d in decorators:
+    if d.decorator_argspec is not None:
+      return _convert_maybe_argspec_to_fullargspec(d.decorator_argspec)
+  return _getfullargspec(target)
+
+
+def getcallargs(*func_and_positional, **named):
+  """TFDecorator-aware replacement for inspect.getcallargs.
+
+  Args:
+    *func_and_positional: A callable, possibly decorated, followed by any
+      positional arguments that would be passed to `func`.
+    **named: The named argument dictionary that would be passed to `func`.
+
+  Returns:
+    A dictionary mapping `func`'s named arguments to the values they would
+    receive if `func(*positional, **named)` were called.
+
+  `getcallargs` will use the argspec from the outermost decorator that provides
+  it. If no attached decorators modify argspec, the final unwrapped target's
+  argspec will be used.
+  """
+  func = func_and_positional[0]
+  positional = func_and_positional[1:]
+  argspec = getfullargspec(func)
+  call_args = named.copy()
+  this = getattr(func, 'im_self', None) or getattr(func, '__self__', None)
+  if ismethod(func) and this:
+    positional = (this,) + positional
+  remaining_positionals = [arg for arg in argspec.args if arg not in call_args]
+  call_args.update(dict(zip(remaining_positionals, positional)))
+  default_count = 0 if not argspec.defaults else len(argspec.defaults)
+  if default_count:
+    for arg, value in zip(argspec.args[-default_count:], argspec.defaults):
+      if arg not in call_args:
+        call_args[arg] = value
+  if argspec.kwonlydefaults is not None:
+    for k, v in argspec.kwonlydefaults.items():
+      if k not in call_args:
+        call_args[k] = v
+  return call_args
+
+
+def getframeinfo(*args, **kwargs):
+  return _inspect.getframeinfo(*args, **kwargs)
+
+
+def getdoc(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getdoc.
+
+  Args:
+    object: An object, possibly decorated.
+
+  Returns:
+    The docstring associated with the object.
+
+  The outermost-decorated object is intended to have the most complete
+  documentation, so the decorated parameter is not unwrapped.
+  """
+  return _inspect.getdoc(object)
+
+
+def getfile(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getfile."""
+  unwrapped_object = tf_decorator.unwrap(object)[1]
+
+  # Work around for the case when object is a stack frame
+  # and only .pyc files are used. In this case, getfile
+  # might return incorrect path. So, we get the path from f_globals
+  # instead.
+  if (hasattr(unwrapped_object, 'f_globals') and
+      '__file__' in unwrapped_object.f_globals):
+    return unwrapped_object.f_globals['__file__']
+  return _inspect.getfile(unwrapped_object)
+
+
+def getmembers(object, predicate=None):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getmembers."""
+  return _inspect.getmembers(object, predicate)
+
+
+def getmodule(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getmodule."""
+  return _inspect.getmodule(object)
+
+
+def getmro(cls):
+  """TFDecorator-aware replacement for inspect.getmro."""
+  return _inspect.getmro(cls)
+
+
+def getsource(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getsource."""
+  return _inspect.getsource(tf_decorator.unwrap(object)[1])
+
+
+def getsourcefile(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getsourcefile."""
+  return _inspect.getsourcefile(tf_decorator.unwrap(object)[1])
+
+
+def getsourcelines(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.getsourcelines."""
+  return _inspect.getsourcelines(tf_decorator.unwrap(object)[1])
+
+
+def isbuiltin(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isbuiltin."""
+  return _inspect.isbuiltin(tf_decorator.unwrap(object)[1])
+
+
+def isclass(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isclass."""
+  return _inspect.isclass(tf_decorator.unwrap(object)[1])
+
+
+def isfunction(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isfunction."""
+  return _inspect.isfunction(tf_decorator.unwrap(object)[1])
+
+
+def isframe(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.ismodule."""
+  return _inspect.isframe(tf_decorator.unwrap(object)[1])
+
+
+def isgenerator(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isgenerator."""
+  return _inspect.isgenerator(tf_decorator.unwrap(object)[1])
+
+
+def isgeneratorfunction(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isgeneratorfunction."""
+  return _inspect.isgeneratorfunction(tf_decorator.unwrap(object)[1])
+
+
+def ismethod(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.ismethod."""
+  return _inspect.ismethod(tf_decorator.unwrap(object)[1])
+
+
+def ismodule(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.ismodule."""
+  return _inspect.ismodule(tf_decorator.unwrap(object)[1])
+
+
+def isroutine(object):  # pylint: disable=redefined-builtin
+  """TFDecorator-aware replacement for inspect.isroutine."""
+  return _inspect.isroutine(tf_decorator.unwrap(object)[1])
+
+
+def stack(context=1):
+  """TFDecorator-aware replacement for inspect.stack."""
+  return _inspect.stack(context)[1:]