STT-tensorflow/tensorflow/python/autograph/impl/api.py

# Copyright 2016 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.
# ==============================================================================
"""This module contains the user-facing API for AutoGraph."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools
import inspect
import os
import sys
import textwrap
import traceback

import six

from tensorflow.python.autograph.core import ag_ctx
from tensorflow.python.autograph.core import converter
from tensorflow.python.autograph.impl import conversion
from tensorflow.python.autograph.operators import py_builtins
from tensorflow.python.autograph.pyct import error_utils
from tensorflow.python.autograph.pyct import errors
from tensorflow.python.autograph.pyct import inspect_utils
from tensorflow.python.autograph.pyct import origin_info
from tensorflow.python.autograph.utils import ag_logging as logging
from tensorflow.python.eager import function
from tensorflow.python.framework import errors_impl
from tensorflow.python.util import tf_decorator
from tensorflow.python.util import tf_inspect
from tensorflow.python.util import tf_stack
from tensorflow.python.util.tf_export import tf_export


def is_autograph_strict_conversion_mode():
  return int(os.environ.get('AUTOGRAPH_STRICT_CONVERSION', '0')) > 0


# TODO(mdan): Export this symbol.
class AutoGraphError(errors.PyCTError):
  """Base class for all AutoGraph exceptions."""
  pass


class ConversionError(AutoGraphError):
  """Raised during the conversion process."""
  pass


class StagingError(AutoGraphError):
  """Raised during the staging (i.e. Python execution) of converted code."""
  pass


class _ErrorMetadata(error_utils.ErrorMetadataBase):
  """AutoGraph-specific error metadata. See base class."""

  def create_exception(self, source_error):
    preferred_type = type(source_error)
    if issubclass(preferred_type, errors_impl.OpError):
      # Best-effort unpacking of OpError exceptions.
      # TODO(mdan): Use a mechanism that is more future-proof.
      init_argspec = tf_inspect.getfullargspec(preferred_type.__init__)
      message = self.get_message()
      init_args = tuple(init_argspec.args)
      # At the time of this writing, TF errors either take 3 or 4 arguments,
      # with the fourth being error_code.
      if init_args == ('self', 'node_def', 'op', 'message', 'error_code'):
        return preferred_type(
            node_def=source_error.node_def,
            op=source_error.op,
            message=message,
            error_code=self.error_code)
      elif init_args == ('self', 'node_def', 'op', 'message'):
        if 'error_code' in init_argspec.kwonlyargs:
          return preferred_type(
              node_def=source_error.node_def,
              op=source_error.op,
              message=message,
              errro_code=self.error_code)
        else:
          return preferred_type(
              node_def=source_error.node_def,
              op=source_error.op,
              message=message)

    elif preferred_type in (errors.PyCTError, AutoGraphError, ConversionError,
                            StagingError, errors_impl.InaccessibleTensorError,
                            errors_impl.OperatorNotAllowedInGraphError):
      return preferred_type(self.get_message())

    exc = super(_ErrorMetadata, self).create_exception(source_error)
    if exc is not None:
      return exc

    # Note: While changing an error's message property to change the message it
    # displays will probably work a lot of times, there is no standard way in
    # Python to do that. The safest way is therefore to create a new exception.
    # For user defined exceptions, we could define an interface that allowed
    # them to work under this mechanism.
    return StagingError(self.get_message())


class StackTraceMapper(tf_stack.StackTraceMapper):
  """Remaps generated code to code it originated from."""

  def __init__(self, converted_fn):
    self._source_map = converted_fn.ag_source_map

  def get_effective_source_map(self):
    effective_source_map = self._effective_source_map
    if effective_source_map is None:
      if self.parent is not None:
        parent_map = self.parent.get_effective_source_map()
      else:
        parent_map = {}

      effective_source_map = {}
      for loc, origin in self._source_map.items():
        effective_source_map[(loc.filename, loc.lineno)] = (
            origin.loc.filename, origin.loc.lineno, origin.function_name)

      for key, value in parent_map.items():
        filename, lineno, _ = value
        value_loc = origin_info.LineLocation(filename=filename, lineno=lineno)
        if value_loc in self._source_map:
          origin = self._source_map[value_loc]
          effective_source_map[key] = (
              origin.loc.filename, origin.loc.lineno, origin.function_name)
        else:
          effective_source_map[key] = value
      self._effective_source_map = effective_source_map
    return effective_source_map


def autograph_artifact(entity, extras=None):
  setattr(entity, 'autograph_info__', extras)
  return entity


def is_autograph_artifact(entity):
  return hasattr(entity, 'autograph_info__')


def tf_convert(f, ctx, convert_by_default=True, user_requested=False):
  """Decorator that applies AutoGraph to a function.

  Use in internal APIs.

  This API is suitable for high order functions internal to the TensorFlow API,
  and more generally any function to which Autograph is not applied.

  Guidance: convert was a decorator meant for use directly by developers, and
  will be soon deprecated in favor of tf.function. tf_convert is to be called
  from high order functions internal to TF.

  Args:
    f: Callable.
    ctx: ag_ctx.ControlStatusCtx, the Autograph context in which `f` is used.
    convert_by_default: bool, whether to use AutoGraph when the context doesn't
      specify.
    user_requested: bool, whether to ignore the conversion whitelist. See
      ConversionOptions.user_requested.

  Returns:
    Either `f or the converted version of `f`.
  """

  if is_autograph_artifact(f):
    return f
  f_wrapper = f
  decorators, f = tf_decorator.unwrap(f)

  # TODO(mdan): Grab features from context.
  # Note: we pass the original context through to convert to properly handle the
  # following scenario, which can be used inside TF implementations:
  #
  #   ctx = ag_ctx.control_status_ctx()
  #   @function(autograph=False)  # Low-level graph code
  #   def inner_fn():
  #     # The context is disabled here, but should be enabled in user user_fn
  #     tf_convert(user_fn, ctx=ctx)
  if ctx.status == ag_ctx.Status.ENABLED:
    wrapper_factory = convert(
        recursive=True, user_requested=user_requested, conversion_ctx=ctx)
  elif ctx.status == ag_ctx.Status.DISABLED:
    wrapper_factory = do_not_convert
  elif ctx.status == ag_ctx.Status.UNSPECIFIED:
    if convert_by_default:
      wrapper_factory = convert(
          recursive=True, user_requested=user_requested, conversion_ctx=ctx)
    else:
      wrapper_factory = call_with_unspecified_conversion_status
  else:
    assert False, 'This switch contains all possible cases!'
  wrapper = wrapper_factory(f)

  if decorators:
    wrapper = tf_decorator.rewrap(f_wrapper, f, wrapper)

  return autograph_artifact(wrapper)


# TODO(mdan): Make private.
def convert(recursive=False,
            optional_features=None,
            user_requested=True,
            conversion_ctx=ag_ctx.NullCtx()):
  """Decorator that compiles a function to use TensorFlow ops.

  The decorator is dynamic - it recompiles the target whenever the decorated
  function is called. This means the parameter values are known at conversion.
  It also means that repeated calls with different types of parameters will be
  correctly processed.

  Args:
    recursive: bool, whether to recursively convert any functions or classes
      that the converted function may use.
    optional_features: converted.Feature, allows toggling optional or
      experimental features. When set to None, only the core features are
      enabled.
    user_requested: bool, whether this is a function that the user explicitly
      asked to be converted. See ConversionOptions.user_requested.
    conversion_ctx: Optional ag_ctx.ControlStatusCtx, the Autograph context in
      which `f` is used.

  Returns:
    Callable, a decorator that converts the given function into an equivalent
    function that uses TensorFlow ops.
  """

  def decorator(f):
    """Decorator implementation."""

    def wrapper(*args, **kwargs):
      """Wrapper that calls the converted version of f."""
      options = converter.ConversionOptions(
          recursive=recursive,
          user_requested=user_requested,
          optional_features=optional_features)
      try:
        with conversion_ctx:
          return converted_call(f, args, kwargs, options=options)
      except Exception as e:  # pylint:disable=broad-except
        if hasattr(e, 'ag_error_metadata'):
          raise e.ag_error_metadata.to_exception(e)
        else:
          raise

    if inspect.isfunction(f) or inspect.ismethod(f):
      wrapper = functools.update_wrapper(wrapper, f)

    decorated_wrapper = tf_decorator.make_decorator(f, wrapper)
    return autograph_artifact(decorated_wrapper)

  return decorator


def call_with_unspecified_conversion_status(func):
  """Decorator that resets the conversion context to the unspecified status."""
  def wrapper(*args, **kwargs):
    with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.UNSPECIFIED):
      return func(*args, **kwargs)

  if inspect.isfunction(func) or inspect.ismethod(func):
    wrapper = functools.update_wrapper(wrapper, func)

  return autograph_artifact(wrapper)


@tf_export('autograph.experimental.do_not_convert')
def do_not_convert(func=None):
  """Decorator that suppresses the conversion of a function.

  Args:
    func: function to decorate.

  Returns:
    If `func` is not None, returns a `Callable` which is equivalent to
    `func`, but is not converted by AutoGraph.
    If `func` is None, returns a decorator that, when invoked with a
    single `func` argument, returns a `Callable` equivalent to the
    above case.
  """
  if func is None:
    return do_not_convert

  def wrapper(*args, **kwargs):
    with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
      return func(*args, **kwargs)

  if inspect.isfunction(func) or inspect.ismethod(func):
    wrapper = functools.update_wrapper(wrapper, func)

  return autograph_artifact(wrapper)


def _attach_metadata(e, f):
  """Augments an error with the metadata necessary for rewrite."""
  if hasattr(e, 'ag_pass_through'):
    return

  metadata = getattr(e, 'ag_error_metadata', None)
  source_map = f.ag_source_map

  if metadata is None:
    logging.log(1, 'Caught error in user callable %s', f, exc_info=True)
    message = '{}: {}'.format(e.__class__.__name__, e)
  else:
    message = None

  cause_tb = traceback.extract_tb(sys.exc_info()[2])[1:]

  e.ag_error_metadata = _ErrorMetadata(
      cause_tb, metadata, message, source_map, __file__)


def _call_unconverted(f, args, kwargs, options, update_cache=True):
  """Calls the original function without converting with AutoGraph."""
  if update_cache:
    conversion.cache_whitelisted(f, options)

  if inspect.ismethod(f) and isinstance(f.__self__, function.TfMethodTarget):
    return f.__self__.call(args, kwargs)

  if kwargs is not None:
    return f(*args, **kwargs)
  return f(*args)


def _is_of_known_loaded_module(f, module_name):
  mod = sys.modules.get(module_name, None)
  if mod is None:
    return False
  if any(v is not None for v in mod.__dict__.values() if f is v):
    return True
  return False


def _is_known_loaded_type(f, module_name, entity_name):
  """Tests whether the function or method is an instance of a known type."""
  if (module_name not in sys.modules or
      not hasattr(sys.modules[module_name], entity_name)):
    return False
  type_entity = getattr(sys.modules[module_name], entity_name)
  if isinstance(f, type_entity):
    # The method if of this type. Example:
    #
    # o = ClassType()
    # function(o.method)()
    return True
  # Note: inspect is required here, to avoid unpacking tf.function decorators.
  if inspect.ismethod(f):
    # The the unbound method if of this type. Example:
    #
    # class ClassType:
    #   @function
    #   def method(self):
    #     ...
    # o = ClassType()
    # o.method()
    if isinstance(f.__func__, type_entity):
      return True
  return False


def _fall_back_unconverted(f, args, kwargs, options, exc):
  """Falls back to calling the function unconverted, in case of error."""
  # TODO(mdan): Consider adding an internal metric.
  warning_template = (
      'AutoGraph could not transform %s and will run it as-is.\n'
      '%s'
      'Cause: %s\n'
      'To silence this warning, decorate the function with'
      ' @tf.autograph.experimental.do_not_convert')
  if isinstance(exc, errors.UnsupportedLanguageElementError):
    if not conversion.is_in_whitelist_cache(f, options):
      logging.warn(warning_template, f, '', exc)
  else:
    file_bug_message = (
        'Please report this to the TensorFlow team. When filing the bug, set'
        ' the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and'
        ' attach the full output.\n')
    logging.warn(warning_template, f, file_bug_message, exc)

  return _call_unconverted(f, args, kwargs, options)


def _log_callargs(f, args, kwargs):
  """Logging helper."""
  logging.log(2, 'Defaults of %s : %s', f, f.__defaults__)
  if not six.PY2:
    logging.log(2, 'KW defaults of %s : %s', f, f.__kwdefaults__)

  if kwargs is not None:
    callargs = tf_inspect.getcallargs(f, *args, **kwargs)
  else:
    callargs = tf_inspect.getcallargs(f, *args)

  formatted_callargs = '\n'.join(
      '    {}: {}'.format(k, v) for k, v in callargs.items())
  logging.log(2, 'Calling %s with\n%s\n', f, formatted_callargs)


def converted_call(f,
                   args,
                   kwargs,
                   caller_fn_scope=None,
                   options=None):
  """Compiles a function call inline.

  For internal use only.

  Note: The argument list is optimized for readability of generated code, which
  may look like this:

    ag__.converted_call(f, (arg1, arg2), None, fscope)
    ag__.converted_call(f, (), dict(arg1=val1, **kwargs), fscope)
    ag__.converted_call(f, (arg1, arg2) + varargs, dict(**kwargs), lscope)

  Args:
    f: The function to convert.
    args: Tuple, the original positional arguments of f
    kwargs: Optional[Dict], the original keyword arguments of f
    caller_fn_scope: Optional[function_wrappers.FunctionScope], the function
      scope of the converted function in which this call was originally made.
    options: Optional[converter.ConversionOptions], conversion options. If not
      specified, the value of caller_fn_scope.callopts is used. Either options
      or caller_fn_scope must be present.

  Returns:
    Any, the result of executing a possibly-converted `f` with the given
      arguments.
  """
  logging.log(1, 'Converted call: %s\n    args: %s\n    kwargs: %s\n', f, args,
              kwargs)

  if options is None:
    if caller_fn_scope is None:
      raise ValueError('either caller_fn_scope or options must have a value')
    options = caller_fn_scope.callopts

  if conversion.is_in_whitelist_cache(f, options):
    logging.log(2, 'Whitelisted %s: from cache', f)
    return _call_unconverted(f, args, kwargs, options, False)

  if ag_ctx.control_status_ctx().status == ag_ctx.Status.DISABLED:
    logging.log(2, 'Whitelisted: %s: AutoGraph is disabled in context', f)
    return _call_unconverted(f, args, kwargs, options, False)

  if is_autograph_artifact(f):
    logging.log(2, 'Permanently whitelisted: %s: AutoGraph artifact', f)
    return _call_unconverted(f, args, kwargs, options)

  # If this is a partial, unwrap it and redo all the checks.
  if isinstance(f, functools.partial):
    new_kwargs = {}
    if f.keywords is not None:
      # Use copy to avoid mutating the underlying keywords.
      new_kwargs = f.keywords.copy()
    if kwargs is not None:
      new_kwargs.update(kwargs)
    new_args = f.args + args
    logging.log(3, 'Forwarding call of partial %s with\n%s\n%s\n', f, new_args,
                new_kwargs)
    return converted_call(
        f.func,
        new_args,
        new_kwargs,
        caller_fn_scope=caller_fn_scope,
        options=options)

  if inspect_utils.isbuiltin(f):
    if f is eval:
      return py_builtins.eval_in_original_context(f, args, caller_fn_scope)
    if f is super:
      return py_builtins.super_in_original_context(f, args, caller_fn_scope)
    if kwargs:
      return py_builtins.overload_of(f)(*args, **kwargs)
    else:
      return py_builtins.overload_of(f)(*args)

  # TODO(b/122265385): Remove this bypass.
  if (_is_known_loaded_type(f, 'wrapt', 'FunctionWrapper') or
      _is_known_loaded_type(f, 'wrapt', 'BoundFunctionWrapper')):
    logging.warn(
        '{} appears to be decorated by wrapt, which is not yet supported'
        ' by AutoGraph. The function will run as-is.'
        ' You may still apply AutoGraph before the wrapt decorator.'.format(f))
    logging.log(2, 'Permanently whitelisted: %s: wrapt decorated', f)
    return _call_unconverted(f, args, kwargs, options)

  if _is_known_loaded_type(f, 'functools', '_lru_cache_wrapper'):
    logging.log(2, 'Permanently whitelisted: %s: lru_cache', f)
    return _call_unconverted(f, args, kwargs, options)

  # Constructors are permanently whitelisted.
  # TODO(mdan): Toggle as experimental feature instead.
  # TODO(b/124016764): Remove this limitation.
  if inspect_utils.isconstructor(f):
    logging.log(2, 'Permanently whitelisted: %s: constructor', f)
    return _call_unconverted(f, args, kwargs, options)

  # Other built-in modules are permanently whitelisted.
  # TODO(mdan): Figure out how to do this consistently for all stdlib modules.
  if any(
      _is_of_known_loaded_module(f, m)
      for m in ('collections', 'pdb', 'copy', 'inspect', 're')):
    logging.log(2, 'Permanently whitelisted: %s: part of builtin module', f)
    return _call_unconverted(f, args, kwargs, options)

  # Custom ops and kernels are also permanently whitelisted.
  # See tensorflow.framework.load_library.
  if (hasattr(f, '__module__') and
      hasattr(f.__module__, '_IS_TENSORFLOW_PLUGIN')):
    logging.log(2, 'Permanently whitelisted: %s: TensorFlow plugin', f)
    return _call_unconverted(f, args, kwargs, options)

  if not options.user_requested and conversion.is_whitelisted(f):
    return _call_unconverted(f, args, kwargs, options)

  # internal_convert_user_code is for example turned off when issuing a dynamic
  # call conversion from generated code while in nonrecursive mode. In that
  # case we evidently don't want to recurse, but we still have to convert
  # things like builtins.
  if not options.internal_convert_user_code:
    return _call_unconverted(f, args, kwargs, options)

  try:
    if inspect.ismethod(f) or inspect.isfunction(f):
      target_entity = f
      effective_args = args

      f_self = getattr(f, '__self__', None)
      if f_self is not None:
        if isinstance(f_self, function.TfMethodTarget):
          f_self = f_self.target
        effective_args = (f_self,) + effective_args

    elif hasattr(f, '__class__') and hasattr(f.__class__, '__call__'):
      # Callable objects. Dunder methods have special lookup rules, see:
      # https://docs.python.org/3/reference/datamodel.html#specialnames
      # TODO(mdan): Recurse into converted_call to simplify other verifications.
      # This should be handled in the same way as partials.
      target_entity = f.__class__.__call__
      effective_args = (f,) + args

    else:
      target_entity = f
      raise NotImplementedError('unknown callable type "%s"' % type(f))

  except Exception as e:  # pylint:disable=broad-except
    logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True)
    if is_autograph_strict_conversion_mode():
      raise
    return _fall_back_unconverted(f, args, kwargs, options, e)

  if not hasattr(target_entity, '__code__'):
    logging.log(2, 'Permanently whitelisted: %s: native binding',
                target_entity)
    return _call_unconverted(f, args, kwargs, options)
  elif (hasattr(target_entity.__code__, 'co_filename') and
        target_entity.__code__.co_filename == '<string>'):
    # TODO(mdan): __globals__['txt'] might work in Py3.
    logging.log(2, 'Permanently whitelisted: %s: dynamic code (exec?)',
                target_entity)
    return _call_unconverted(f, args, kwargs, options)

  try:
    program_ctx = converter.ProgramContext(
        options=options, autograph_module=tf_inspect.getmodule(converted_call))
    converted_f = conversion.convert(target_entity, program_ctx)
    if logging.has_verbosity(2):
      _log_callargs(converted_f, effective_args, kwargs)
  except Exception as e:  # pylint:disable=broad-except
    logging.log(1, 'Error transforming entity %s', target_entity, exc_info=True)
    if is_autograph_strict_conversion_mode():
      raise
    return _fall_back_unconverted(f, args, kwargs, options, e)

  with StackTraceMapper(converted_f), tf_stack.CurrentModuleFilter():
    try:
      if kwargs is not None:
        result = converted_f(*effective_args, **kwargs)
      else:
        result = converted_f(*effective_args)
    except Exception as e:
      _attach_metadata(e, converted_f)
      raise

  return result


# pylint:disable=line-too-long
@tf_export('autograph.to_graph', v1=[])
def to_graph(entity, recursive=True, experimental_optional_features=None):
  """Converts a Python entity into a TensorFlow graph.

  Also see: `tf.autograph.to_code`, `tf.function`.

  Unlike `tf.function`, `to_graph` is a low-level transpiler that converts
  Python code to TensorFlow graph code. It does not implement any caching,
  variable management or create any actual ops, and is best used where greater
  control over the generated TensorFlow graph is desired. Another difference
  from `tf.function` is that `to_graph` will not wrap the graph into a
  TensorFlow function or a Python callable. Internally, `tf.function` uses
  `to_graph`.

  Example usage:

  >>> def f(x):
  ...   if x > 0:
  ...     y = x * x
  ...   else:
  ...     y = -x
  ...   return y
  ...
  >>> converted_f = to_graph(f)
  >>> x = tf.constant(2)
  >>> converted_f(x)  # converted_foo is like a TensorFlow Op.
  <tf.Tensor: shape=(), dtype=int32, numpy=4>

  Supported Python entities include:
    * functions
    * classes
    * object methods

  Functions are converted into new functions with converted code.

  Classes are converted by generating a new class whose methods use converted
  code.

  Methods are converted into unbound function that have an additional first
  argument called `self`.

  For a tutorial, see the
  [tf.function and AutoGraph guide](https://www.tensorflow.org/guide/function).
  For more detailed information, see the
  [AutoGraph reference documentation](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/autograph/g3doc/reference/index.md).

  Args:
    entity: Python callable or class to convert.
    recursive: Whether to recursively convert any functions that the converted
      function may call.
    experimental_optional_features: `None`, a tuple of, or a single
      `tf.autograph.experimental.Feature` value.

  Returns:
    Same as `entity`, the converted Python function or class.

  Raises:
    ValueError: If the entity could not be converted.
  """
  try:
    program_ctx = converter.ProgramContext(
        options=converter.ConversionOptions(
            recursive=recursive,
            user_requested=True,
            optional_features=experimental_optional_features),
        autograph_module=tf_inspect.getmodule(to_graph))
    return autograph_artifact(conversion.convert(entity, program_ctx))
  except (ValueError, AttributeError, KeyError, NameError, AssertionError) as e:
    logging.error(1, 'Error converting %s', entity, exc_info=True)
    raise ConversionError('converting {}: {}: {}'.format(
        entity, e.__class__.__name__, str(e)))


@tf_export(v1=['autograph.to_graph'])
def to_graph_v1(entity,
                recursive=True,
                arg_values=None,
                arg_types=None,
                experimental_optional_features=None):
  """Converts a Python entity into a TensorFlow graph.

  Also see: `tf.autograph.to_code`, `tf.function`.

  Unlike `tf.function`, `to_graph` is a low-level transpiler that converts
  Python code to TensorFlow graph code. It does not implement any caching,
  variable management or create any actual ops, and is best used where greater
  control over the generated TensorFlow graph is desired. Another difference
  from `tf.function` is that `to_graph` will not wrap the graph into a
  TensorFlow function or a Python callable. Internally, `tf.function` uses
  `to_graph`.

  _Example Usage_

  ```python
    def foo(x):
      if x > 0:
        y = x * x
      else:
        y = -x
      return y

    converted_foo = to_graph(foo)

    x = tf.constant(1)
    y = converted_foo(x)  # converted_foo is a TensorFlow Op-like.
    assert is_tensor(y)
  ```

  Supported Python entities include:
    * functions
    * classes
    * object methods

  Functions are converted into new functions with converted code.

  Classes are converted by generating a new class whose methods use converted
  code.

  Methods are converted into unbound function that have an additional first
  argument called `self`.

  Args:
    entity: Python callable or class to convert.
    recursive: Whether to recursively convert any functions that the converted
      function may call.
    arg_values: Deprecated.
    arg_types: Deprecated.
    experimental_optional_features: `None`, a tuple of, or a single
      `tf.autograph.experimental.Feature` value.

  Returns:
    Same as `entity`, the converted Python function or class.

  Raises:
    ValueError: If the entity could not be converted.
  """
  del arg_types
  del arg_values
  return to_graph(
      entity,
      recursive=recursive,
      experimental_optional_features=experimental_optional_features)


@tf_export(v1=['autograph.to_code'])
def to_code_v1(entity,
               recursive=True,
               arg_values=None,
               arg_types=None,
               indentation='  ',
               experimental_optional_features=None):
  """Returns the source code generated by AutoGraph, as a string.

  Example usage:

  >>> def f(x):
  ...   if x < 0:
  ...     x = -x
  ...   return x
  >>> tf.autograph.to_code(f)
  "...def tf__f(x):..."

  Also see: `tf.autograph.to_graph`.

  Note: If a function has been decorated with `tf.function`, pass its
  underlying Python function, rather than the callable that `tf.function
  creates:

  >>> @tf.function
  ... def f(x):
  ...   if x < 0:
  ...     x = -x
  ...   return x
  >>> tf.autograph.to_code(f.python_function)
  "...def tf__f(x):..."

  Args:
    entity: Python callable or class.
    recursive: Whether to recursively convert any functions that the converted
      function may call.
    arg_values: Deprecated.
    arg_types: Deprecated.
    indentation: Deprecated.
    experimental_optional_features: `None`, a tuple of, or a single
      `tf.autograph.experimental.Feature` value.

  Returns:
    The converted code as string.
  """
  del arg_values
  del arg_types
  del indentation
  return to_code(
      entity,
      recursive=recursive,
      experimental_optional_features=experimental_optional_features)


@tf_export('autograph.to_code', v1=[])
def to_code(entity, recursive=True, experimental_optional_features=None):
  """Returns the source code generated by AutoGraph, as a string.

  Example usage:

  >>> def f(x):
  ...   if x < 0:
  ...     x = -x
  ...   return x
  >>> tf.autograph.to_code(f)
  "...def tf__f(x):..."

  Also see: `tf.autograph.to_graph`.

  Note: If a function has been decorated with `tf.function`, pass its
  underlying Python function, rather than the callable that `tf.function
  creates:

  >>> @tf.function
  ... def f(x):
  ...   if x < 0:
  ...     x = -x
  ...   return x
  >>> tf.autograph.to_code(f.python_function)
  "...def tf__f(x):..."

  Args:
    entity: Python callable or class to convert.
    recursive: Whether to recursively convert any functions that the converted
      function may call.
    experimental_optional_features: `None`, a tuple of, or a single
      `tf.autograph.experimental.Feature` value.

  Returns:
    The converted code as string.
  """
  source = tf_inspect.getsource(
      to_graph(
          entity,
          recursive=recursive,
          experimental_optional_features=experimental_optional_features))
  return textwrap.dedent(source)