text: #   Copyright (c) 2018 PaddlePaddle 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.
  
  from __future__ import print_function
  
  import collections
  from collections import defaultdict
  from collections import Iterable
  import contextlib
  from .wrapped_decorator import signature_safe_contextmanager, wrap_decorator
  import os
  import re
  import traceback
  import six
  import copy
  from types import MethodType, FunctionType
  
  import numpy as np
  import subprocess
  import multiprocessing
  import sys
  import logging
  from .. import compat as cpt
  from .proto import framework_pb2
  
  from . import core
  from . import unique_name
  import paddle.version as fluid_version
  import warnings
  import functools
  
  __all__ = [
      'Program',
      'default_startup_program',
      'default_main_program',
      'program_guard',
      'name_scope',
      'cuda_places',
      'cpu_places',
      'xpu_places',
      'cuda_pinned_places',
      'in_dygraph_mode',
      'is_compiled_with_cuda',
      'is_compiled_with_xpu',
      'Variable',
      'require_version',
      'device_guard',
      'set_flags',
      'get_flags',
  ]
  
  EMPTY_VAR_NAME = core.kEmptyVarName()
  TEMP_VAR_NAME = core.kTempVarName()
  GRAD_VAR_SUFFIX = core.kGradVarSuffix()
  ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
  CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
  
  _dygraph_tracer_ = None
  _global_expected_place_ = None
  _current_device = None
  global_prog_seed = 0
  
  
  def require_version(min_version, max_version=None):
      """
          Check if the installed version of PaddlePaddle is in [min_version, max_version],
          if the installed version is lower than ``min_version`` or higher than ``max_version``,
          an exception will be thrown, NO returns if the installed version is satisfied.
  
          Args:
              min_version (str): the minimum version required (like '1.4.0').
              max_version (str, optional): the max version required (like '1.6.0'), default is None,
                  meaning any version equal or higher than ``min_version`` is acceptable.
  
          Returns:
              None.
  
          Raises:
              TypeError: if the type of ``min_version`` is not str.
              TypeError: if the type of ``max_version`` is not str or type(None).
              ValueError: if the value of ``min_version`` is not in version format.
              ValueError: if the value of ``max_version`` is not in version format or None.
              Exception: if the installed version is lower than ``min_version`` or higher than ``max_version``.
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
  
                  # any version >= 0.1.0 is acceptable.
                  fluid.require_version('0.1.0')
  
                  # if 0.1.0 <= version <= 10.0.0, it is acceptable.
                  fluid.require_version(min_version='0.1.0', max_version='10.0.0')
          """
      if not isinstance(min_version, str):
          raise TypeError(
              "The type of 'min_version' in require_version must be str, but received %s."
              % (type(min_version)))
  
      if not isinstance(max_version, (str, type(None))):
          raise TypeError(
              "The type of 'max_version' in require_version must be str or type(None), but received %s."
              % (type(max_version)))
  
      check_format = re.match(r'\d+(\.\d+){0,3}', min_version)
      if check_format is None or check_format.group() != min_version:
          raise ValueError(
              "The value of 'min_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', "
              "like '1.5.2.0', but received %s" % min_version)
  
      if max_version is not None:
          check_format = re.match(r'\d+(\.\d+){0,3}', max_version)
          if check_format is None or check_format.group() != max_version:
              raise ValueError(
                  "The value of 'max_version' in require_version must be in format '\\d+(\\.\\d+){0,3}', "
                  "like '1.5.2.0', but received %s" % max_version)
  
      version_installed = [
          fluid_version.major, fluid_version.minor, fluid_version.patch,
          fluid_version.rc
      ]
      zero_version = ['0', '0', '0', '0']
  
      def version_cmp(ver_a, ver_b):
          for i in six.moves.range(len(ver_a)):
              if int(ver_a[i]) > int(ver_b[i]):
                  return 1
              elif int(ver_a[i]) < int(ver_b[i]):
                  return -1
          return 0
  
      if version_cmp(version_installed, zero_version) == 0:
          if max_version is not None:
              warnings.warn(
                  "PaddlePaddle version in [%s, %s] required, but %s installed. "
                  "Maybe you are using a develop version, "
                  "please make sure the version is good with your code." %
                  (min_version, max_version, fluid_version.full_version))
          else:
              warnings.warn(
                  "PaddlePaddle version %s or higher is required, but %s installed, "
                  "Maybe you are using a develop version, "
                  "please make sure the version is good with your code." %
                  (min_version, fluid_version.full_version))
          return
  
      min_version_split = min_version.split('.')
      min_version_to_check = min_version_split + zero_version[len(
          min_version_split):]
  
      if max_version is not None:
          max_version_split = max_version.split('.')
          max_version_to_check = max_version_split + zero_version[len(
              max_version_split):]
  
          if version_cmp(version_installed,
                         max_version_to_check) > 0 or version_cmp(
                             version_installed, min_version_to_check) < 0:
              raise Exception(
                  "VersionError: PaddlePaddle version in [%s, %s] required, but %s installed."
                  % (min_version, max_version, fluid_version.full_version))
      else:
          if version_cmp(version_installed, min_version_to_check) < 0:
              raise Exception(
                  "VersionError: PaddlePaddle version %s or higher is required, but %s installed, "
                  "please upgrade your PaddlePaddle to %s or other higher version."
                  % (min_version, fluid_version.full_version, min_version))
  
  
  def in_dygraph_mode():
      """
  
      .. note::
          Dynamic graph mode is turn ON by default since paddle 2.0.0
  
      This API checks whether paddle runs in dynamic graph mode.
  
      You can turn ON static graph mode by `enable_static <../dygraph/base/disable_dygraph_en.html>`_ ,
      and turn OFF static graph mode by `disable_static <../dygraph/base/enable_dygraph_en.html>`_  .
  
      Returns:
          bool: Whether paddle runs in dynamic graph mode.
  
      Examples:
          .. code-block:: python
  
              import paddle
              print(paddle.in_dynamic_mode())  # True, dynamic mode is turn ON by default since paddle 2.0.0
  
              paddle.enable_static()
              print(paddle.in_dynamic_mode())  # False, Now we are in static mode
  
              paddle.disable_static()
              print(paddle.in_dynamic_mode())  # True, Now we are in dynamic mode
  
      """
      return _dygraph_tracer_ is not None
  
  
  def _dygraph_not_support_(func):
      def __impl__(*args, **kwargs):
          assert not in_dygraph_mode(
          ), "We don't support %s in imperative mode" % func.__name__
          return func(*args, **kwargs)
  
      return __impl__
  
  
  def _dygraph_only_(func):
      def __impl__(*args, **kwargs):
          assert in_dygraph_mode(
          ), "We only support '%s()' in dynamic graph mode, please call 'paddle.disable_static()' to enter dynamic graph mode." % func.__name__
          return func(*args, **kwargs)
  
      return __impl__
  
  
  def _static_only_(func):
      def __impl__(*args, **kwargs):
          assert not in_dygraph_mode(
          ), "In PaddlePaddle 2.x, we turn on dynamic graph mode by default, and '%s()' is only supported in static graph mode. So if you want to use this api, please call 'paddle.enable_static()' before this api to enter static graph mode." % func.__name__
          return func(*args, **kwargs)
  
      return __impl__
  
  
  # NOTE(zhiqiu): This decorator is used for the APIs of Variable which is only
  # used to make Variable and VarBase has same interfaces, like numpy. Since VarBase is not exposed in our
  # official docments, logically, we want to keep VarBase and logically consistent. While, actually,
  # in our implementation, there some APIs not supported, like numpy, because Variable contains the desc.
  # So, those APIs are listed under class Variable to generate docs only.
  # TODO(zhiqiu): We should make VarBase consistent with Variable in future, for example, by inheritting
  # same base class.
  def _fake_interface_only_(func):
      def __impl__(*args, **kwargs):
          raise AssertionError(
              "'%s' only can be called by `paddle.Tensor` in dynamic graph mode. Suggestions:\n"
              "  1. If you are in static graph mode, you can switch to dynamic graph mode by turning off `paddle.enable_static()` or calling `paddle.disable_static()`.\n"
              "  2. If you are using `@paddle.jit.to_static`, you can turn off ProgramTranslator by calling `paddle.jit.ProgramTranslator().enable(False)`. "
              "If you have to translate dynamic graph to static graph, please use other API to replace '%s'."
              % (func.__name__, func.__name__))
  
      return __impl__
  
  
  # NOTE(chenweihang): There is argument name typo (stat_dict, correct name is state_dict)
  # in fluid api Layer.set_dict, Optimizer.load, in order to correct the argument without
  # introducing compatibility issues, add this decorator
  # NOTE(chenweihang): not using `wrap_decorator` here is because `wrap_decorator` will
  # move kwargs to args, which doesn't work in this decorate case
  def deprecate_stat_dict(func):
      @functools.wraps(func)
      def wrapper(*args, **kwargs):
          if 'stat_dict' in kwargs:
              warnings.warn(
                  "The argument `stat_dict` has deprecated, please change it to `state_dict`.",
                  DeprecationWarning)
              kwargs['state_dict'] = kwargs['stat_dict']
              kwargs.pop('stat_dict')
          return func(*args, **kwargs)
  
      return wrapper
  
  
  dygraph_not_support = wrap_decorator(_dygraph_not_support_)
  dygraph_only = wrap_decorator(_dygraph_only_)
  static_only = wrap_decorator(_static_only_)
  fake_interface_only = wrap_decorator(_fake_interface_only_)
  
  
  def _dygraph_tracer():
      return _dygraph_tracer_
  
  
  def _current_expected_place():
      global _global_expected_place_
      if _global_expected_place_ is None:
          if core.is_compiled_with_cuda():
              try:
                  device_count = core.get_cuda_device_count()
              except Exception as e:
                  device_count = 0
              if device_count > 0:
                  _global_expected_place_ = core.CUDAPlace(0)
              else:
                  warnings.warn(
                      "You are using GPU version Paddle, but your CUDA device is not set properly. CPU device will be used by default."
                  )
                  _global_expected_place_ = core.CPUPlace()
          else:
              _global_expected_place_ = core.CPUPlace()
  
      return _global_expected_place_
  
  
  def _set_dygraph_tracer_expected_place(place):
      global _dygraph_tracer_
      if _dygraph_tracer_ is not None:
          _dygraph_tracer_._expected_place = place
  
  
  def _set_expected_place(place):
      global _global_expected_place_
      _global_expected_place_ = place
      _set_dygraph_tracer_expected_place(place)
  
  
  # TODO(zhiqiu): remove this function.
  def _var_base_to_np(var_base):
      """	
      convert VarBase tp numpy	
  
      Args:	
          var_base(VarBase) : the VarBase to convert	
      Returns (np.ndarray): the np.ndarray contain the value of VarBase	
      """
  
      warnings.warn(
          "paddle.fluid.framework._var_base_to_np is deprecated, please use var_base.numpy() instead of _var_base_to_np(var_base)."
      )
  
      return var_base.numpy()
  
  
  def _cpu_num():
      if "CPU_NUM" not in os.environ.keys():
          if multiprocessing.cpu_count() > 1:
              sys.stderr.write(
                  '!!! The CPU_NUM is not specified, you should set CPU_NUM in the environment variable list.\n'
                  'CPU_NUM indicates that how many CPUPlace are used in the current task.\n'
                  'And if this parameter are set as N (equal to the number of physical CPU core) the program may be faster.\n\n'
                  'export CPU_NUM={} # for example, set CPU_NUM as number of physical CPU core which is {}.\n\n'
                  '!!! The default number of CPU_NUM=1.\n'.format(
                      multiprocessing.cpu_count(), multiprocessing.cpu_count()))
          os.environ['CPU_NUM'] = str(1)
      cpu_num = os.environ.get('CPU_NUM')
      return int(cpu_num)
  
  
  def _cuda_ids():
      gpus_env = os.getenv("FLAGS_selected_gpus")
      if gpus_env:
          device_ids = [int(s) for s in gpus_env.split(",")]
      else:
          device_ids = six.moves.range(core.get_cuda_device_count())
      return device_ids
  
  
  def _xpu_ids():
      xpus_env = os.getenv("FLAGS_selected_xpus")
      if xpus_env:
          device_ids = [int(s) for s in xpus_env.split(",")]
      else:
          device_ids = six.moves.range(core.get_xpu_device_count())
      return device_ids
  
  
  def is_compiled_with_xpu():
      """
      Whether this whl package can be used to run the model on XPU.
  
      Returns (bool): support xpu or not.
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
              support_xpu = fluid.is_compiled_with_xpu()
      """
      return core.is_compiled_with_xpu()
  
  
  def is_compiled_with_cuda():
      """
      Whether this whl package can be used to run the model on GPU.
  
      Returns (bool): `True` if CUDA is currently available, otherwise `False`.
  
      Examples:
          .. code-block:: python
  
              import paddle
              support_gpu = paddle.is_compiled_with_cuda()
      """
      return core.is_compiled_with_cuda()
  
  
  def cuda_places(device_ids=None):
      """
      **Note**:
          For multi-card tasks, please use `FLAGS_selected_gpus` environment variable to set the visible GPU device.
          The next version will fix the problem with `CUDA_VISIBLE_DEVICES` environment variable.
  
      This function creates a list of :code:`paddle.CUDAPlace` objects.
  
      If :code:`device_ids` is None, environment variable of
      :code:`FLAGS_selected_gpus` would be checked first. For example, if
      :code:`FLAGS_selected_gpus=0,1,2`, the returned list would
      be [paddle.CUDAPlace(0), paddle.CUDAPlace(1), paddle.CUDAPlace(2)].
      If :code:`FLAGS_selected_gpus` is not set, all visible
      gpu places would be returned according to the :code:`CUDA_VISIBLE_DEVICES` environment variable.
  
      If :code:`device_ids` is not None, it should be the device
      ids of GPUs. For example, if :code:`device_ids=[0,1,2]`,
      the returned list would be 
      [paddle.CUDAPlace(0), paddle.CUDAPlace(1), paddle.CUDAPlace(2)].
  
      Parameters:
          device_ids (list|tuple, optional): A list/tuple of int of GPU device ids.
  
      Returns:
          list of paddle.CUDAPlace: Created GPU place list.
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.static as static
  
              # required: gpu
              
              paddle.enable_static()
  
              cuda_places = static.cuda_places()
  
      """
      assert core.is_compiled_with_cuda(), \
          "Not compiled with CUDA"
      if device_ids is None:
          device_ids = _cuda_ids()
      elif not isinstance(device_ids, (list, tuple)):
          device_ids = [device_ids]
      return [core.CUDAPlace(dev_id) for dev_id in device_ids]
  
  
  def xpu_places(device_ids=None):
      """
      **Note**:
          For multi-card tasks, please use `FLAGS_selected_xpus` environment variable to set the visible XPU device.
      This function creates a list of :code:`paddle.XPUPlace` objects.
      If :code:`device_ids` is None, environment variable of
      :code:`FLAGS_selected_xpus` would be checked first. For example, if
      :code:`FLAGS_selected_xpus=0,1,2`, the returned list would
      be [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
      If :code:`FLAGS_selected_xpus` is not set, all visible
      xpu places would be returned.
      If :code:`device_ids` is not None, it should be the device
      ids of XPUs. For example, if :code:`device_ids=[0,1,2]`,
      the returned list would be 
      [paddle.XPUPlace(0), paddle.XPUPlace(1), paddle.XPUPlace(2)].
      
      Parameters:
          device_ids (list or tuple of int, optional): list of XPU device ids.
      Returns:
          list of paddle.XPUPlace: Created XPU place list.
      Examples:
          .. code-block:: python
          
              import paddle
              import paddle.static as static
              
              paddle.enable_static()
              xpu_places = static.xpu_places()
      """
      assert core.is_compiled_with_xpu(), \
          "Not compiled with XPU"
      if device_ids is None:
          device_ids = _xpu_ids()
      elif not isinstance(device_ids, (list, tuple)):
          device_ids = [device_ids]
      return [core.XPUPlace(dev_id) for dev_id in device_ids]
  
  
  def cpu_places(device_count=None):
      """
      This function creates a list of :code:`paddle.CPUPlace` objects, and returns the created list.
  
      If :code:`device_count` is None, the device count would
      be determined by environment variable :code:`CPU_NUM`. 
      If :code:`CPU_NUM` is not set, the default value is 1,
      i.e. CPU_NUM=1.
      :code:`CPU_NUM` indicates the number of devices used in the current task.
      The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores.
  
      Parameters:
          device_count (int, optional): device number. Default: None.
  
      Returns:
          list of paddle.CPUPlace: Created list of CPU places.
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.static as static
  
              paddle.enable_static()
  
              cpu_places = static.cpu_places()
      """
  
      if device_count is None:
          device_count = _cpu_num()
      return [core.CPUPlace()] * device_count
  
  
  def cuda_pinned_places(device_count=None):
      """
      This function creates a list of :code:`fluid.CUDAPinnedPlace` objects.
  
      If :code:`device_count` is None, the device count would
      be determined by environment variable :code:`CPU_NUM`. 
      If :code:`CPU_NUM` is not set, the default value is 1,
      i.e. CPU_NUM=1.
      :code:`CPU_NUM` indicates the number of devices used in the current task.
      The running of the program can be accelerated if :code:`CPU_NUM` is the same as the number of physical cores.
  
      Parameters:
          device_count (int, optional): device number. Default: None.
  
      Returns:
          list of fluid.CUDAPinnedPlace: Created list of CUDA pinned places.
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
              cuda_pinned_places_cpu_num = fluid.cuda_pinned_places()
              # or
              cuda_pinned_places = fluid.cuda_pinned_places(1)
  
      """
      assert core.is_compiled_with_cuda(), \
          "Not compiled with CUDA"
      if device_count is None:
          device_count = len(_cuda_ids())
      return [core.CUDAPinnedPlace()] * device_count
  
  
  class NameScope(object):
      def __init__(self, name="", parent=None):
          self._children = dict()
          self._name = name
          self._parent = parent
  
      def child(self, prefix):
          if prefix not in self._children:
              new_child = NameScope(prefix, self)
              self._children[prefix] = [new_child]
          else:
              new_child = NameScope(prefix + "_%d" % len(self._children[prefix]),
                                    self)
              self._children[prefix].append(new_child)
          return new_child
  
      def parent(self):
          return self._parent
  
      def name(self):
          return self._name
  
  
  _name_scope = NameScope()
  
  
  @signature_safe_contextmanager
  def name_scope(prefix=None):
      """
      :api_attr: Static Graph
  
      Generate hierarchical name prefix for the operators in Static Graph.
  
      Note: 
          This should only used for debugging and visualization purpose.
          Don't use it for serious analysis such as graph/program transformations.
          Don't use it in dygraph, since it will cause memory leak.
  
      Args:
          prefix(str, optional): prefix. Default is none.
  
      Examples:
          .. code-block:: python
  
            import paddle
            paddle.enable_static()
            with paddle.static.name_scope("s1"):
               a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
               b = a + 1
               with paddle.static.name_scope("s2"):
                  c = b * 1
               with paddle.static.name_scope("s3"):
                  d = c / 1
            with paddle.static.name_scope("s1"):
                  f = paddle.tensor.pow(d, 2.0)
            with paddle.static.name_scope("s4"):
                  g = f - 1
  
            # Op are created in the default main program.  
            for op in paddle.static.default_main_program().block(0).ops:
                # elementwise_add is created in /s1/
                if op.type == 'elementwise_add':
                    assert op.desc.attr("op_namescope") == '/s1/'
                # elementwise_mul is created in '/s1/s2'
                elif op.type == 'elementwise_mul':
                    assert op.desc.attr("op_namescope") == '/s1/s2/'
                # elementwise_div is created in '/s1/s3'
                elif op.type == 'elementwise_div':
                    assert op.desc.attr("op_namescope") == '/s1/s3/'
                # elementwise_sum is created in '/s4'
                elif op.type == 'elementwise_sub':
                    assert op.desc.attr("op_namescope") == '/s4/'
                # pow is created in /s1_1/
                elif op.type == 'pow':
                    assert op.desc.attr("op_namescope") == '/s1_1/'
      """
      # TODO(panyx0718): Only [0-9a-z].
      # in dygraph we don't need namescope since it will cause mem leak
      if in_dygraph_mode():
          yield
      else:
          assert prefix, "namescope prefix can not be empty."
          global _name_scope
          _name_scope = _name_scope.child(prefix)
          try:
              yield
          finally:
              _name_scope = _name_scope.parent()
  
  
  def _full_name_scope():
      global _name_scope
      scope = _name_scope
      name = ""
      while scope:
          name = scope.name() + "/" + name
          scope = scope.parent()
      return name
  
  
  def generate_control_dev_var_name():
      import random
      return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
  
  
  def grad_var_name(var_name):
      """
      Returns:
          str: gradient name for a certain var name
      """
      return var_name + GRAD_VAR_SUFFIX
  
  
  def convert_np_dtype_to_dtype_(np_dtype):
      """
      Convert the data type in numpy to the data type in Paddle
  
      Args:
          np_dtype(np.dtype): the data type in numpy.
  
      Returns:
          core.VarDesc.VarType: the data type in Paddle.
  
      """
      dtype = np.dtype(np_dtype)
      if dtype == np.float32:
          return core.VarDesc.VarType.FP32
      elif dtype == np.float64:
          return core.VarDesc.VarType.FP64
      elif dtype == np.float16:
          return core.VarDesc.VarType.FP16
      elif dtype == np.int32:
          return core.VarDesc.VarType.INT32
      elif dtype == np.int16:
          return core.VarDesc.VarType.INT16
      elif dtype == np.int64:
          return core.VarDesc.VarType.INT64
      elif dtype == np.bool:
          return core.VarDesc.VarType.BOOL
      elif dtype == np.uint16:
          # since there is still no support for bfloat16 in NumPy,
          # uint16 is used for casting bfloat16
          return core.VarDesc.VarType.BF16
      elif dtype == np.uint8:
          return core.VarDesc.VarType.UINT8
      elif dtype == np.int8:
          return core.VarDesc.VarType.INT8
      elif dtype == np.complex64:
          return core.VarDesc.VarType.COMPLEX64
      elif dtype == np.complex128:
          return core.VarDesc.VarType.COMPLEX128
      else:
          raise ValueError("Not supported numpy dtype %s" % dtype)
  
  
  def dtype_is_floating(dtype):
      """
      Check the data type is floating or not.
      Args:
          dtype(np.dtype|core.VarDesc.VarType): data type.
              Could be numpy format or Paddle format
  
      Returns(bool): True if data type is a float value
  
      """
      if not isinstance(dtype, core.VarDesc.VarType):
          dtype = convert_np_dtype_to_dtype_(dtype)
  
      return dtype in [
          core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32,
          core.VarDesc.VarType.FP64
      ]
  
  
  def _debug_string_(proto, throw_on_error=True):
      """
      Get the debug string of a protobuf message. The message could be not
      initialized.
      Args:
          proto(google.protobuf.message.Message): The protobuf message
          throw_on_error(bool): True if raise an error when the protobuf message
              is not initialized.
  
      Returns(str): The debug string of the protobuf message
  
      """
      error_fields = list()
      if not proto.IsInitialized(error_fields) and throw_on_error:
          raise ValueError("{0} are not initialized.\nThe message is {1}:\n".
                           format(error_fields, proto))
      return proto.__str__()
  
  
  def _varbase_creator(type=core.VarDesc.VarType.LOD_TENSOR,
                       name=None,
                       shape=None,
                       dtype=None,
                       persistable=None,
                       **kwargs):
      if dtype is not None:
          if not isinstance(dtype, core.VarDesc.VarType):
              dtype = convert_np_dtype_to_dtype_(dtype)
  
      return core.VarBase(dtype if dtype else core.VarDesc.VarType.FP32,
                          list(shape) if shape else [], name, type
                          if type else core.VarDesc.VarType.LOD_TENSOR, True
                          if persistable else False)
  
  
  class VariableMetaClass(type):
      @classmethod
      def __instancecheck__(cls, instance):
          t = type(instance)
          if in_dygraph_mode():
              return issubclass(t, core.VarBase)
          else:
              return issubclass(t, Variable)
  
  
  class ParameterMetaClass(VariableMetaClass):
      @classmethod
      def __instancecheck__(cls, instance):
          t = type(instance)
          if in_dygraph_mode():
              return issubclass(t, ParamBase)
          else:
              return issubclass(t, Parameter)
  
  
  def _getitem_impl_(var, item):
      """
      Slice the variable.
  
      Args:
          item(int/slice/tuple) : the index.
  
      Returns:
          Sliced variable
      """
  
      if not isinstance(item, tuple):
          item = [item]
  
      decrease_axis = []
      slice_axis = []
      slice_start = []
      slice_end = []
      slice_step = []
      use_strided_slice = False
      reverse_axis = []
      target_block = default_main_program().current_block()
  
      def fill_constant(shape, value, force_cpu=False, out=None):
          var.block.append_op(
              type='fill_constant',
              inputs={},
              outputs={'Out': [out]},
              attrs={
                  'shape': shape,
                  'dtype': out.dtype,
                  'value': float(value),
                  'force_cpu': force_cpu
              })
          out.stop_gradient = True
          return out
  
      for dim, slice_item in enumerate(item):
          if isinstance(slice_item, slice):
              start = slice_item.start
              end = slice_item.stop
              step = slice_item.step
  
              if start is None and end is None and step is None:
                  continue
  
              if step is None:
                  step = 1
  
              if start is None and end is None:
                  assert (step == -1)
                  reverse_axis.append(dim)
                  continue
  
              if start is None:
                  start = 0
  
              if end is None:
                  end = 10000000
  
              if step != 1:
                  use_strided_slice = True
  
              slice_axis.append(dim)
              slice_start.append(start)
              slice_end.append(end)
              slice_step.append(step)
          else:
              decrease_axis.append(dim)
              slice_axis.append(dim)
              slice_start.append(slice_item)
              slice_step.append(1)
              if isinstance(slice_item, Variable):
                  temp_1 = var.block.create_var(dtype=slice_item.dtype)
                  fill_constant([1], 1, force_cpu=True, out=temp_1)
                  temp_end = target_block.create_var(dtype=slice_item.dtype)
                  target_block.append_op(
                      type='elementwise_add',
                      inputs={'X': slice_item,
                              'Y': temp_1},
                      outputs={'Out': temp_end},
                      attrs={'axis': -1})
                  slice_end.append(temp_end)
              else:
                  slice_end.append(slice_item + 1
                                   if slice_item != -1 else 10000000)
  
      def contain_var(one_list):
          for ele in one_list:
              if isinstance(ele, Variable):
                  return True
          return False
  
      def get_new_list_tensor(old_list):
          new_list_tensor = []
          for dim in old_list:
              if isinstance(dim, Variable):
                  dim.stop_gradient = True
                  new_list_tensor.append(dim)
              else:
                  assert (isinstance(dim, int))
                  temp_out = var.block.create_var(dtype='int64')
                  fill_constant([1], dim, force_cpu=True, out=temp_out)
                  new_list_tensor.append(temp_out)
          return new_list_tensor
  
      inputs = {'Input': [var]}
      attrs = {
          'axes': slice_axis,
          'starts': [],
          'ends': [],
          'decrease_axis': decrease_axis
      }
      if (use_strided_slice == True):
          attrs['strides'] = []
      infer_flags = list(1 for i in range(len(slice_axis)))
  
      # starts
      if contain_var(slice_start):
          inputs['StartsTensorList'] = get_new_list_tensor(slice_start)
          for i, dim in enumerate(slice_start):
              if isinstance(dim, Variable):
                  attrs['starts'].append(-1)
                  infer_flags[i] = -1
              else:
                  attrs['starts'].append(dim)
      else:
          attrs['starts'] = slice_start
  
      # ends
      if contain_var(slice_end):
          inputs['EndsTensorList'] = get_new_list_tensor(slice_end)
          for i, dim in enumerate(slice_end):
              if isinstance(dim, Variable):
                  attrs['ends'].append(-1)
                  infer_flags[i] = -1
              else:
                  attrs['ends'].append(dim)
      else:
          attrs['ends'] = slice_end
  
      # strides
      if use_strided_slice == True:
          if contain_var(slice_step):
              inputs['StridesTensorList'] = get_new_list_tensor(slice_step)
              for i, dim in enumerate(slice_step):
                  if isinstance(dim, Variable):
                      attrs['strides'].append(-1)
                      infer_flags[i] = -1
                  else:
                      attrs['strides'].append(dim)
          else:
              attrs['strides'] = slice_step
      # infer_flags
      attrs['infer_flags'] = infer_flags
  
      out = var
      if use_strided_slice == False and len(slice_axis) > 0:
          # append slice_op here
          slice_out_var = target_block.create_var(
              name=unique_name.generate_with_ignorable_key(var.name + "_slice"),
              dtype=var.dtype)
  
          target_block.append_op(
              type="slice",
              inputs=inputs,
              outputs={'Out': [slice_out_var]},
              attrs=attrs)
  
          out = slice_out_var
      elif use_strided_slice == True and len(slice_axis) > 0:
          strided_slice_out_var = target_block.create_var(
              name=unique_name.generate_with_ignorable_key(var.name +
                                                           "_strided_slice"),
              dtype=var.dtype)
          target_block.append_op(
              type="strided_slice",
              inputs=inputs,
              outputs={'Out': [strided_slice_out_var]},
              attrs=attrs)
  
          out = strided_slice_out_var
  
      if len(reverse_axis) > 0:
          reverse_out_var = target_block.create_var(
              name=unique_name.generate_with_ignorable_key(var.name +
                                                           "_slice_reverse"),
              dtype=var.dtype)
          target_block.append_op(
              type="reverse",
              inputs={'X': out},
              outputs={'Out': [reverse_out_var]},
              attrs={'axis': reverse_axis})
  
          out = reverse_out_var
  
      return out
  
  
  @six.add_metaclass(VariableMetaClass)
  class Variable(object):
      """
      **Notes**:
          **The constructor of Variable should not be invoked directly.**
  
          **In Static Graph Mode: Please use** `Block.create_var` **to create a Static variable which has no data until being feed.**
  
          **In Dygraph Mode: Please use** :ref:`api_fluid_dygraph_to_variable` **to create a dygraph variable with real data**
  
      In Fluid, every input and output of an OP is a variable. In most
      cases, variables are used for holding different kinds of data or training
      labels. A variable belongs to a :ref:`api_guide_Block_en` . All variable has its own name and
      two variables in different :ref:`api_guide_Block_en` could have the same name.
  
      There are many kinds of variables. Each kind of them has its own attributes
      and usages. Please refer to the `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_ for details.
  
      Most of a Variable's member variables can be set to be None. It mean
      it is not available or will be specified later.
  
      Examples:
          In Static Graph Mode:
  
          .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
          In `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_  Mode:
  
          .. code-block:: python
  
              import paddle.fluid as fluid
              import numpy as np
  
              with fluid.dygraph.guard():
                  new_variable = fluid.dygraph.to_variable(np.arange(10))
  
      """
  
      def __init__(self,
                   block,
                   type=core.VarDesc.VarType.LOD_TENSOR,
                   name=None,
                   shape=None,
                   dtype=None,
                   lod_level=None,
                   capacity=None,
                   persistable=None,
                   error_clip=None,
                   stop_gradient=False,
                   is_data=False,
                   need_check_feed=False,
                   belong_to_optimizer=False,
                   **kwargs):
          self.block = block
          if name is None:
              name = unique_name.generate('_generated_var')
  
          if dtype is not None:
              if not isinstance(dtype, core.VarDesc.VarType):
                  dtype = convert_np_dtype_to_dtype_(dtype)
  
          self.belong_to_optimizer = belong_to_optimizer
  
          self.error_clip = error_clip
  
          is_new_var = False
          name = cpt.to_text(name)
          self.desc = self.block.desc.find_var(cpt.to_bytes(name))
  
          if self.desc is None:
              self.desc = self.block.desc.var(cpt.to_bytes(name))
              is_new_var = True
  
          if is_new_var:
              self.desc.set_type(type)
          elif self.desc.type() != type:
              raise ValueError("Variable '{0}' has been created before. The "
                               "previous type is {1}, the new type is {2}. They"
                               " are not matched".format(self.name,
                                                         self.desc.type(), type))
  
          if shape is not None:
              if is_new_var:
                  self.desc.set_shape(shape)
              else:
                  old_shape = self.shape
                  shape = tuple(shape)
                  if shape != old_shape:
                      raise ValueError(
                          "Variable '{0}' has been created before. The previous "
                          "shape is {1}, the new shape is {2}. They are not "
                          "matched.".format(self.name, old_shape, shape))
          if dtype is not None:
              if is_new_var:
                  self.desc.set_dtype(dtype)
              else:
                  old_dtype = self.dtype
                  if dtype != old_dtype:
                      raise ValueError("Variable '{0}' has been created before. "
                                       "The previous data type is {1}, the new "
                                       "data type is {2}. They are not "
                                       "matched.".format(self.name, old_dtype,
                                                         dtype))
  
          if lod_level is not None:
              if is_new_var:
                  self.desc.set_lod_level(lod_level)
              else:
                  if lod_level != self.lod_level:
                      raise ValueError("Variable '{0}' has been created before. "
                                       "The previous lod_level is {1}, the new "
                                       "lod_level is {2}. They are not "
                                       "matched".format(self.name, self.lod_level,
                                                        lod_level))
          if persistable is not None:
              if is_new_var:
                  self.desc.set_persistable(persistable)
              else:
                  if persistable != self.persistable:
                      raise ValueError(
                          "Variable '{0}' has been created before."
                          "The previous persistable is {1}, the new "
                          "persistable is {2}. They are not matched".format(
                              self.name, self.persistable, persistable))
  
          if need_check_feed and is_new_var:
              self.desc.set_need_check_feed(need_check_feed)
  
          if capacity is not None:
              if is_new_var:
                  self.desc.set_capacity(capacity)
              else:
                  # TODO(abhinavarora) : Compare with set capacity once,
                  # get_capacity is implemented
                  pass
  
          self.block.vars[name] = self
          self.op = None
          self._stop_gradient = stop_gradient
          self.is_data = is_data
  
      @fake_interface_only
      def detach(self):
          """
          **Notes**:
              **This API is ONLY available in Dygraph mode**
  
          Returns a new Variable, detached from the current graph.
  
          Returns:
               ( :ref:`api_guide_Variable_en` | dtype is same as current Variable): The detached Variable.
  
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  from paddle.fluid.dygraph.base import to_variable
                  from paddle.fluid.dygraph import Linear
                  import numpy as np
  
                  data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                  with fluid.dygraph.guard():
                      linear = Linear(32, 64)
                      data = to_variable(data)
                      x = linear(data)
                      y = x.detach()
  
          """
          pass
  
      @fake_interface_only
      def numpy(self):
          """
          **Notes**:
              **This API is ONLY available in Dygraph mode**
  
          Returns a numpy array shows the value of current :ref:`api_guide_Variable_en`
  
          Returns:
              ndarray: The numpy value of current Variable.
  
          Returns type:
              ndarray: dtype is same as current Variable
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  from paddle.fluid.dygraph.base import to_variable
                  from paddle.fluid.dygraph import Linear
                  import numpy as np
  
                  data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                  with fluid.dygraph.guard():
                      linear = Linear(32, 64)
                      data = to_variable(data)
                      x = linear(data)
                      print(x.numpy())
  
          """
          pass
  
      @fake_interface_only
      def backward(self, retain_graph=False):
          """
          **Notes**:
              **This API is ONLY available in Dygraph mode**
  
          Run backward of current Graph which starts from current Tensor.
  
          Args:
              retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would
                  like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter
                  :code:`retain_graph` to True, then the grads will be retained. Thus, seting it to False is much more memory-efficient.
                  Defaults to False.
  
          Returns:
              NoneType: None
  
          Examples:
              .. code-block:: python
  
                  import numpy as np
                  import paddle
                  paddle.disable_static()
  
                  x = np.ones([2, 2], np.float32)
                  inputs = []
                  for _ in range(10):
                      tmp = paddle.to_tensor(x)
                      # if we don't set tmp's stop_gradient as False then, all path to loss will has no gradient since
                      # there is no one need gradient on it.
                      tmp.stop_gradient=False
                      inputs.append(tmp)
                  ret = paddle.add_n(inputs)
                  loss = paddle.sum(ret)
                  loss.backward()
  
          """
          pass
  
      @fake_interface_only
      def gradient(self):
          """
          **Notes**:
              **This API is ONLY available in Dygraph mode**
  
          Get the Gradient of Current Variable
  
          Returns:
              ndarray or tuple of ndarray: if Variable's type is LoDTensor, return numpy value of the gradient of current Variable, if Variable's type is SelectedRows, return tuple of ndarray, first element of tuple is numpy value of the gradient of current Variable, second element of tuple is numpy value of the rows of current Variable.
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import numpy as np
  
                  # example1: return ndarray
                  x = np.ones([2, 2], np.float32)
                  with fluid.dygraph.guard():
                      inputs2 = []
                      for _ in range(10):
                          tmp = fluid.dygraph.base.to_variable(x)
                          tmp.stop_gradient=False
                          inputs2.append(tmp)
                      ret2 = fluid.layers.sums(inputs2)
                      loss2 = fluid.layers.reduce_sum(ret2)
                      loss2.backward()
                      print(loss2.gradient())
  
                  # example2: return tuple of ndarray
                  with fluid.dygraph.guard():
                      embedding = fluid.dygraph.Embedding(
                          size=[20, 32],
                          param_attr='emb.w',
                          is_sparse=True)
                      x_data = np.arange(12).reshape(4, 3).astype('int64')
                      x_data = x_data.reshape((-1, 3, 1))
                      x = fluid.dygraph.base.to_variable(x_data)
                      out = embedding(x)
                      out.backward()
                      print(embedding.weight.gradient())
  
          """
          pass
  
      @fake_interface_only
      def clear_gradient(self):
          """
          **Notes**:
              **1. This API is ONLY available in Dygraph mode**
  
              **2. Use it only Variable has gradient, normally we use this for Parameters since other temporal Variable will be deleted by Python's GC**
  
          Clear  (set to ``0`` ) the Gradient of Current Variable
  
          Returns:  None
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import numpy as np
  
                  x = np.ones([2, 2], np.float32)
                  with fluid.dygraph.guard():
                      inputs2 = []
                      for _ in range(10):
                          tmp = fluid.dygraph.base.to_variable(x)
                          tmp.stop_gradient=False
                          inputs2.append(tmp)
                      ret2 = fluid.layers.sums(inputs2)
                      loss2 = fluid.layers.reduce_sum(ret2)
                      loss2.backward()
                      print(loss2.gradient())
                      loss2.clear_gradient()
                      print("After clear {}".format(loss2.gradient()))
  
          """
          pass
  
      @fake_interface_only
      def register_hook(self, hook):
          pass
  
      def __str__(self):
          return self._to_readable_code()
  
      def _to_readable_code(self):
          """
          Get readable debug string of Variable.
  
          .. note::
              If you want to get the debug string in protobuf format,
              please use :code:`to_string` method.
  
          Returns:
              string: The formatted Variable string.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  cur_program = static.Program()
                  cur_block = cur_program.current_block()
                  new_variable = cur_block.create_var(name="X",
                                                      shape=[-1, 23, 48],
                                                      dtype='float32')
                  print(new_variable._to_readable_code())
          """
          # VarType.LOD_TENSOR -> LOD_TENSOR
          type_str = str(self.type).split('.')[1]
          if self.type == core.VarDesc.VarType.SELECTED_ROWS or self.type == core.VarDesc.VarType.LOD_TENSOR:
              dtype_str = str(self.dtype).split('.')[1]
              var_str = "{name} : {type}.shape{shape}.dtype({dtype}).stop_gradient({stop_gradient})".\
                  format(name=self.name, type=type_str, shape=self.shape,
                         dtype=dtype_str, stop_gradient=self.stop_gradient)
          else:
              var_str = "{name} : {type})".\
                  format(name=self.name, type=type_str)
  
          if type(self) == Parameter:
              if self.trainable:
                  var_str = "trainable param " + var_str
              else:
                  var_str = "param " + var_str
          else:
              var_str = "var " + var_str
  
          if self.persistable:
              var_str = "persist " + var_str
  
          return var_str
  
      def to_string(self, throw_on_error, with_details=False):
          """
          Get debug string.
  
          Args:
  
              throw_on_error (bool): True if raise an exception when self is not initialized.
  
              with_details (bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default value is False;
  
          Returns:
              str: The debug string.
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  import paddle
  
                  paddle.enable_static()
                  cur_program = fluid.Program()
                  cur_block = cur_program.current_block()
                  new_variable = cur_block.create_var(name="X",
                                                      shape=[-1, 23, 48],
                                                      dtype='float32')
                  print(new_variable.to_string(True))
                  print("=============with detail===============")
                  print(new_variable.to_string(True, True))
          """
          assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                 bool)
          protostr = self.desc.serialize_to_string()
          proto = framework_pb2.VarDesc.FromString(six.binary_type(protostr))
          res_str = _debug_string_(proto, throw_on_error)
          if with_details:
              additional_attr = ("error_clip", "stop_gradient")
              for attr_name in additional_attr:
                  res_str += "%s: %s\n" % (attr_name,
                                           cpt.to_text(getattr(self, attr_name)))
  
          return res_str
  
      __repr__ = __str__
  
      @property
      def stop_gradient(self):
          """
          Indicating if we stop gradient from current Variable
  
          **Notes: This Property has default value as** ``True`` **in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, while Parameter's default value is False. However, in Static Graph Mode all Variable's default stop_gradient value is** ``False``
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              import numpy as np
  
              with fluid.dygraph.guard():
                  value0 = np.arange(26).reshape(2, 13).astype("float32")
                  value1 = np.arange(6).reshape(2, 3).astype("float32")
                  value2 = np.arange(10).reshape(2, 5).astype("float32")
                  linear = fluid.Linear(13, 5, dtype="float32")
                  linear2 = fluid.Linear(3, 3, dtype="float32")
                  a = fluid.dygraph.to_variable(value0)
                  b = fluid.dygraph.to_variable(value1)
                  c = fluid.dygraph.to_variable(value2)
                  out1 = linear(a)
                  out2 = linear2(b)
                  out1.stop_gradient = True
                  out = fluid.layers.concat(input=[out1, out2, c], axis=1)
                  out.backward()
  
                  assert linear.weight.gradient() is None
                  assert (out1.gradient() == 0).all()
          """
          return self._stop_gradient
  
      @stop_gradient.setter
      def stop_gradient(self, s):
          self._stop_gradient = s
  
      @property
      def persistable(self):
          """
          Indicating if we current Variable should be long-term alive
  
  
          **Notes: This Property will be deprecated and this API is just to help user understand concept**
  
              **1. All Variable's persistable is** ``False`` **except Parameters.**
  
              **2. In** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, this property should not be changed**
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
              print("persistable of current Var is: {}".format(new_variable.persistable))
          """
          return self.desc.persistable()
  
      @persistable.setter
      def persistable(self, p):
          self.desc.set_persistable(p)
  
      @property
      def name(self):
          """
          Indicating name of current Variable
  
          **Notes: If it has two or more Varaible share the same name in the same** :ref:`api_guide_Block_en` **, it means these Variable will share content in no-** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode. This is how we achieve Parameter sharing**
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
              print("name of current Var is: {}".format(new_variable.name))
          """
          return cpt.to_text(self.desc.name())
  
      @property
      def grad_name(self):
          """
          Indicating name of the gradient Variable of current Variable.
  
          **Notes: This is a read-only property. It simply returns name of
            gradient Variable from a naming convention but doesn't guarantee
            the gradient exists.**
  
          Examples:
            .. code-block:: python
  
            import paddle.fluid as fluid
  
            x = fluid.data(name="x", shape=[-1, 23, 48], dtype='float32')
            print(x.grad_name) # output is "x@GRAD"
  
          """
          return self.name + "@GRAD"
  
      @name.setter
      def name(self, new_name):
          self.desc.set_name(new_name)
  
      @property
      def shape(self):
          """
          Indicating shape of current Variable
  
          **Notes: This is a read-only property**
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
              print("shape of current Var is: {}".format(new_variable.shape))
  
          """
          # convert to tuple, make it as same as numpy API.
          return tuple(self.desc.shape())
  
      @property
      def dtype(self):
          """
          Indicating data type of current Variable
  
          **Notes: This is a read-only property**
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
              print("Dtype of current Var is: {}".format(new_variable.dtype))
          """
          return self.desc.dtype()
  
      @property
      def lod_level(self):
          """
          Indicating ``LoD`` info of current Variable, please refer to  :ref:`api_fluid_LoDTensor_en` to check the meaning
          of ``LoD``
  
          **Notes**:
  
              **1. This is a read-only property**
  
              **2. Don't support this property in** `Dygraph <../../user_guides/howto/dygraph/DyGraph.html>`_ **mode, it's value should be** ``0(int)``
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
              print("LoD Level of current Var is: {}".format(new_variable.lod_level))
          """
          if self.type == core.VarDesc.VarType.SELECTED_ROWS:
              raise Exception("SelectedRows DO NOT supprt lod")
  
          return self.desc.lod_level()
  
      @property
      def type(self):
          """
          Indicating Type of current Variable
  
          **Notes: This is a read-only property**
  
          Examples:
            .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_variable = cur_block.create_var(name="X",
                                                  shape=[-1, 23, 48],
                                                  dtype='float32')
              print("Type of current Var is: {}".format(new_variable.type))
          """
          return self.desc.type()
  
      def clone(self):
          """
          Returns a new static Variable, which is the clone of the original static
          Variable. It remains in the current graph, that is, the cloned Variable 
          provides gradient propagation. Calling ``out = tensor.clone()`` is same
          as ``out = assign(tensor)`` .
  
          Returns:
              Variable: The cloned Variable.
  
          Examples:
              .. code-block:: python
  
                  import paddle
  
                  paddle.enable_static()
  
                  # create a static Variable
                  x = paddle.static.data(name='x', shape=[3, 2, 1])
                  # create a cloned Variable
                  y = x.clone()
  
          """
          output = self.block.create_var(
              name=unique_name.generate_with_ignorable_key(self.name + "_clone"),
              dtype=self.dtype,
              type=self.type,
              persistable=self.persistable,
              stop_gradient=self.stop_gradient)
  
          self.block.append_op(
              type='assign', inputs={'X': [self]}, outputs={'Out': [output]})
          return output
  
      def _set_error_clip(self, error_clip):
          """
          Set the error_clip.
  
          Args:
              error_clip(BaseErrorClipAttr) : The new error_clip.
  
          Returns:
              None
          """
          self.error_clip = error_clip
  
      def _set_info(self, key, value):
          """
          Set key-value information for this variable.
  
          Args:
              key(str): Key for this information.
              value(object): The value associated to the key.
  
          Returns: 
              None
          """
          if not hasattr(self, "_info"):
              self._info = {}
          self._info[key] = value
  
      def _get_info(self, key):
          """
          Get the information of this variable corresponding to key.
  
          Args:
              key(str): Key for this information.
  
          Returns: 
              object
          """
          if hasattr(self, "_info") and key in self._info:
              return self._info[key]
          return None
  
      def _slice_indices(self, slice, length):
          """
          Reference implementation for the slice.indices method.
          """
          # Compute step and length as integers.
          step = 1 if slice.step is None else slice.step
  
          # Raise ValueError for negative length or zero step.
          if length < 0:
              raise ValueError("length should not be negative")
          if step == 0:
              raise ValueError("slice step can not be zero")
  
          # Find lower and upper bounds for start and stop.
          lower = -1 if step < 0 else 0
          upper = length - 1 if step < 0 else length
  
          # Compute start.
          if slice.start is None:
              start = upper if step < 0 else lower
          else:
              start = slice.start
              start = max(start + length, lower) if start < 0 else min(start,
                                                                       upper)
  
          # Compute stop.
          if slice.stop is None:
              stop = lower if step < 0 else upper
          else:
              stop = slice.stop
              stop = max(stop + length, lower) if stop < 0 else min(stop, upper)
  
          return start, stop, step
  
      def _detectEllipsis(self, item):
          has_ellipsis = False
          start = 0
          end = len(self.shape)
          for index, o in enumerate(item):
              if o is Ellipsis:
                  if has_ellipsis:
                      raise ValueError("Index can have one ellipsis only.")
                  has_ellipsis = True
                  start = index
              else:
                  if has_ellipsis:
                      end = index
          return has_ellipsis, start, end
  
      def _reconstructSliceinfo(self, item):
          has_ellipsis, start, end = self._detectEllipsis(item)
          if has_ellipsis:
              newitem = []
              for i in range(start):
                  newitem.append(item[i])
              for i in range(start, end):
                  newitem.append(slice(None, None, None))
              for i in range(end, len(item)):
                  newitem.append(item[i])
              return newitem
          else:
              return None
  
      def _detectContinuesSlice(self, item):
          starts = []
          ends = []
          for index, o in enumerate(item):
              if isinstance(o, int):
                  start = int(o)
                  if (index > 0 and index >= self.shape[index]) \
                          or (index < 0 and (index + self.shape[index]) < 0):
                      raise IndexError("invalid index")
                  start = max(start + self.shape[index], 0) if start < 0 else min(
                      start, self.shape[index])
                  starts.append(start)
                  ends.append(start + 1)
              elif isinstance(o, slice):
                  start, stop, step = self._slice_indices(o, self.shape[index])
                  if step == 1 or step == -1:
                      starts.append(start)
                      ends.append(stop)
                  else:
                      return False, None
              else:
                  raise IndexError("Valid index accept int or slice or ellipsis")
          return True, [starts, ends]
  
      def _cloneVar(self, copy=False):
          if not copy:
              return self.block.create_var(
                  name=unique_name.generate_with_ignorable_key(self.name),
                  dtype=self.dtype)
          else:
              return self
  
      def _sliceVar(self, axes, starts, ends):
          new_var = self._cloneVar()
          self.block.append_op(
              type="slice",
              inputs={'Input': [self]},
              outputs={'Out': [new_var]},
              attrs={'axes': axes,
                     'starts': starts,
                     'ends': ends})
          return new_var
  
      def _concatVar(self, inputs, axis):
          new_var = self._cloneVar()
          self.block.append_op(
              type="concat",
              inputs={'X': inputs},
              outputs={'Out': [new_var]},
              attrs={'axis': axis, })
          return new_var
  
      def _sliceAndConcatVar(self, item, axis):
          if isinstance(item, slice):
              if self.shape[axis] < 0:
                  return self._cloneVar(True)
              start, stop, step = self._slice_indices(item, self.shape[axis])
              if step == 1:
                  return self._sliceVar([axis], [start], [stop])
              else:
                  vars = []
                  if step > 0:
                      while start < stop:
                          vars.append(
                              self._sliceVar([axis], [start], [start + 1]))
                          start += step
                  else:
                      while start > stop:
                          vars.append(
                              self._sliceVar([axis], [start], [start + 1]))
                          start += step
                  return self._concatVar(vars, axis)
          elif isinstance(item, int):
              if self.shape[axis] < 0:
                  return self._cloneVar(True)
              index = int(item)
              if (index > 0 and index >= self.shape[axis]) \
                      or (index < 0 and (index + self.shape[axis]) < 0):
                  raise IndexError("invalid index")
              return self._sliceVar([axis], [index], [index + 1])
          else:
              raise IndexError("Valid index accept int or slice or tuple")
  
      def __getitem__(self, item):
          return _getitem_impl_(self, item)
  
      def __setitem__(self, item, value):
          inputs = {'Input': self}
  
          # 1. Parse item
          if not isinstance(item, tuple):
              item = [item]
  
          decrease_axes = []
          axes = []
          starts = []
          ends = []
          steps = []
  
          max_integer = sys.maxsize
  
          def replace_ellipsis(item):
              # Use slice(None) to replace Ellipsis.
              # For var, var.shape = [3,4,5,6]
              #
              #   var[..., 1:2] -> var[:, :, :, 1:2]
              #   var[0, ...] -> var[0]
              #   var[0, ..., 1:2] -> var[0, :, :, 1:2]
  
              item = list(item)
  
              # Remove Variable to skip bug when counting Ellipsis
              item_remove_var = [
                  ele for ele in item if not isinstance(ele, Variable)
              ]
              ell_count = item_remove_var.count(Ellipsis)
              if ell_count == 0:
                  return item
              elif ell_count > 1:
                  raise IndexError(
                      "An index can only have a single ellipsis ('...')")
  
              ell_idx = item.index(Ellipsis)
  
              if ell_idx == len(item) - 1:
                  return item[:-1]
              else:
                  item[ell_idx:ell_idx + 1] = [slice(None)] * (
                      len(self.shape) - len(item) + 1)
  
              return item
  
          item = replace_ellipsis(item)
  
          for dim, slice_item in enumerate(item):
              if isinstance(slice_item, slice):
                  start = slice_item.start
                  end = slice_item.stop
                  step = slice_item.step
  
                  if start is None and end is None and step is None:
                      continue
  
                  step = 1 if step is None else step
  
                  # TODO: support cases when step < 1
                  if not isinstance(step, Variable) and step == 0:
                      raise ValueError(
                          "When assign a value to a paddle.Tensor, step can not be 0, "
                          "but received step is {}.".format(step))
  
                  if isinstance(step, Variable) and (start is None or
                                                     end is None):
                      raise ValueError(
                          "When assign a value to a paddle.Tensor, it's not supported that "
                          "the start or end is None when the type of step is paddle.Tensor."
                      )
  
                  if start is None:
                      start = 0 if step > 0 else max_integer
  
                  if end is None:
                      end = max_integer if step > 0 else (0 - max_integer)
              else:
                  decrease_axes.append(dim)
                  start = slice_item
                  end = slice_item + 1 if slice_item != -1 else max_integer
                  step = 1
  
              axes.append(dim)
              starts.append(start)
              ends.append(end)
              steps.append(step)
  
          attrs = {
              'axes': axes,
              'starts': starts,
              'ends': ends,
              'steps': steps,
              'decrease_axes': decrease_axes
          }
  
          from .layers import utils
          if utils._contain_var(starts):
              inputs['StartsTensorList'] = utils._convert_to_tensor_list(starts)
              del attrs['starts']
          if utils._contain_var(ends):
              inputs['EndsTensorList'] = utils._convert_to_tensor_list(ends)
              del attrs['ends']
          if utils._contain_var(steps):
              inputs['StepsTensorList'] = utils._convert_to_tensor_list(steps)
              del attrs['steps']
  
          # 2. Parse value
          dtype = self.dtype
          attrs['dtype'] = dtype
  
          from .data_feeder import convert_dtype
          #  2.1 value is an integer of float
          if isinstance(value, (int, float)):
              value = np.array([value]).astype(convert_dtype(dtype))
  
          #  2.2 value is a np.ndarray
          if isinstance(value, np.ndarray):
              shape = list(value.shape)
              if dtype == core.VarDesc.VarType.BOOL:
                  value_name = "bool_values"
                  values = [bool(v) for v in value.flat]
              elif dtype == core.VarDesc.VarType.FP32:
                  value_name = "fp32_values"
                  values = [float(v) for v in value.flat]
              elif dtype == core.VarDesc.VarType.FP64:
                  value_name = "fp64_values"
                  values = [float(v) for v in value.flat]
              elif dtype == core.VarDesc.VarType.INT32:
                  value_name = "int32_values"
                  values = [int(v) for v in value.flat]
              elif dtype == core.VarDesc.VarType.INT64:
                  value_name = "int64_values"
                  values = [int(v) for v in value.flat]
              else:
                  raise TypeError(
                      "When assign a numpy.ndarray, integer or float to a paddle.Tensor, "
                      "the data type of the paddle.Tensor must be bool, float32, int32 or int64, but "
                      "received %s." % convert_dtype(dtype))
              attrs[value_name] = values
              attrs["shape"] = shape
  
          elif isinstance(value, Variable):
              inputs["ValueTensor"] = value
          else:
              raise TypeError(
                  "Only support to assign an integer, float, numpy.ndarray or "
                  "paddle.Tensor to a paddle.Tensor, but received {}".format(
                      type(value)))
  
          cur_block = default_main_program().current_block()
          cur_block.append_op(
              type="set_value", inputs=inputs, outputs={'Out': self}, attrs=attrs)
  
          return self
  
      def get_value(self, scope=None):
          """
          Get the value of variable in given scope. 
  
          Args:
              scope(Scope, optional) : If `scope` is None, it will be set to global scope 
                  obtained through 'paddle.static.global_scope()'. Otherwise, use `scope`.
                  Default: None
  
          Returns:
              Tensor: the value in given scope.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static 
                  import numpy as np
  
                  paddle.enable_static()
  
                  x = static.data(name="x", shape=[10, 10], dtype='float32')
  
                  y = static.nn.fc(x, 10, name='fc')
                  place = paddle.CPUPlace()
                  exe = static.Executor(place)
                  prog = paddle.static.default_main_program()
                  exe.run(static.default_startup_program())
                  inputs = np.ones((10, 10), dtype='float32')
                  exe.run(prog, feed={'x': inputs}, fetch_list=[y, ])
                  path = 'temp/tensor_'
                  for var in prog.list_vars():
                      if var.persistable:
                          t = var.get_value()
                          paddle.save(t, path+var.name+'.pdtensor')
  
                  for var in prog.list_vars():
                      if var.persistable:
                          t_load = paddle.load(path+var.name+'.pdtensor')
                          var.set_value(t_load)
          """
          # The 'framework' is a low-level module, and 'executor' 
          # can not be imported at the begainning of this file. 
          # Therefore, the above two modules are dynamically imported.
          from .executor import global_scope
          if scope is not None and not isinstance(scope, core._Scope):
              raise TypeError(
                  "`scope` should be None or `paddle.static.Scope` type, but received {}.".
                  format(type(scope)))
  
          if scope is None:
              scope = global_scope()
          var_temp = scope.find_var(self.name)
          if var_temp is None:
              raise ValueError("Can not find Variable '{}' in the Scope.".format(
                  self.name))
          t = var_temp.get_tensor()
          return t
  
      def set_value(self, value, scope=None):
          '''
          Set the value to the tensor in given scope. 
  
          Args:
              value(Tensor/ndarray) : The value to be set.
              scope(Scope, optional) : If `scope` is None, it will be set to global scope 
                  obtained through 'paddle.static.global_scope()'. Otherwise, use `scope`.
                  Default: None
  
          Returns:
              None
          
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static 
                  import numpy as np
  
                  paddle.enable_static()
  
                  x = static.data(name="x", shape=[10, 10], dtype='float32')
  
                  y = static.nn.fc(x, 10, name='fc')
                  place = paddle.CPUPlace()
                  exe = static.Executor(place)
                  prog = paddle.static.default_main_program()
                  exe.run(static.default_startup_program())
                  inputs = np.ones((10, 10), dtype='float32')
                  exe.run(prog, feed={'x': inputs}, fetch_list=[y, ])
                  path = 'temp/tensor_'
                  for var in prog.list_vars():
                      if var.persistable:
                          t = var.get_value()
                          paddle.save(t, path+var.name+'.pdtensor')
  
                  for var in prog.list_vars():
                      if var.persistable:
                          t_load = paddle.load(path+var.name+'.pdtensor')
                          var.set_value(t_load)
          '''
  
          # The 'framework' is a low-level module, and 'executor'
          # can not be imported at the begainning of this file. 
          # Therefore, the above two modules are dynamically imported.
          from .executor import global_scope
  
          if not (isinstance(value, np.ndarray) or hasattr(value, '__array__')):
              raise TypeError(
                  "`value` should be `numpy.ndarray` or `LoDTensor`, but received {}.".
                  format(type(value)))
  
          if scope is not None and not isinstance(scope, core._Scope):
              raise TypeError(
                  "`scope` should be None or `paddle.static.Scope` type, but received {}.".
                  format(type(scope)))
  
          if scope is None:
              scope = global_scope()
  
          var_temp = scope.find_var(self.name)
          if var_temp is None:
              raise ValueError("Can not find Variable '{}' in the Scope.".format(
                  self.name))
  
          t = var_temp.get_tensor()
  
          if hasattr(value, 'shape'):
              if isinstance(value.shape, (MethodType, FunctionType)):
                  value_shape = value.shape()
              else:
                  value_shape = value.shape
              if list(t.shape()) != list(value_shape):
                  raise ValueError(
                      "{} expected a shape {}, but the received shape is {}.".
                      format(self.name, list(t.shape()), list(value_shape)))
  
          p = t._place()
          if p.is_cpu_place():
              place = core.CPUPlace()
          elif p.is_cuda_pinned_place():
              place = core.CUDAPinnedPlace()
          elif p.is_xpu_place():
              p = core.Place()
              p.set_place(t._place())
              place = core.XPUPlace(p.xpu_device_id())
          else:
              p = core.Place()
              p.set_place(t._place())
              place = core.CUDAPlace(p.gpu_device_id())
  
          t.set(value, place)
  
  
  def get_all_op_protos():
      """
      Get all registered op proto from PaddlePaddle C++ end.
  
      Returns:
         list: list of OpProto.
      """
      protostrs = core.get_all_op_protos()
      ret_values = []
      for pbstr in protostrs:
          op_proto = framework_pb2.OpProto.FromString(six.binary_type(pbstr))
          ret_values.append(op_proto)
      return ret_values
  
  
  class OpProtoHolder(object):
      """
      A global variable to hold all OpProtos from C++ as a map
      """
  
      @classmethod
      def instance(cls):
          if not hasattr(cls, '_instance'):
              cls._instance = cls()
          return cls._instance
  
      def __init__(self):
          assert not hasattr(
              self.__class__,
              '_instance'), 'Please use `instance()` to get OpProtoHolder object!'
          op_protos = get_all_op_protos()
          self.op_proto_map = {}
          for proto in op_protos:
              self.op_proto_map[proto.type] = proto
  
      def get_op_proto(self, type):
          """
          Get OpProto by a type string.
          Args:
              type(str): The type that operator registered in C++ side.
  
          Returns(framework_pb2.OpProto): The OpProto
  
          """
          if type not in self.op_proto_map:
              raise ValueError("Operator \"%s\" has not been registered." % type)
          return self.op_proto_map[type]
  
      def update_op_proto(self):
          op_protos = get_all_op_protos()
          custom_op_names = []
          for proto in op_protos:
              if proto.type not in self.op_proto_map:
                  self.op_proto_map[proto.type] = proto
                  custom_op_names.append(proto.type)
  
          return custom_op_names
  
      @staticmethod
      def generated_op_attr_names():
          return {
              core.op_proto_and_checker_maker.kOpRoleAttrName(),
              core.op_proto_and_checker_maker.kOpRoleVarAttrName(),
              core.op_proto_and_checker_maker.kOpNameScopeAttrName(),
              core.op_proto_and_checker_maker.kOpCreationCallstackAttrName(),
              core.op_proto_and_checker_maker.kOpDeviceAttrName()
          }
  
  
  class Operator(object):
      """
      In Fluid, all the operation are represented by Operator, and Operator
      is regarded as a build in an instruction of a Block. Users can use the
      build in instructions to describe their neural network.
  
      Args:
          block(Block): The block has the current operator.
          desc(core.OpDesc): The protobuf description of Operator.
          type(str): The type of operator. Default None.
          inputs(dict): The input of this Operator. it is a dictionary, for every
              element, key is the input parameter name, and value is a list of
              variables. Default None.
          outputs(dict): The output of this Operator. it is a dictionary, for
              every element, key is the input parameter name, and value is a list
              of variables. Default None.
          attrs(dict): The attributes of this Operator. it is a dictionary, for
              every element, key is attribute name, and value is the attribute value.
              The attribute type should be as same as the type registered in C++ side.
              Default None.
  
      Returns:
          Operator: The initialized Operator.
  
      Raises:
          ValueError: If the passed input, output and attrs doesn't match the
              initializing Operator's that registered in C++ side.
  
      Notes:
          The constructor of operator should not be invoked directly. Use
          Block.append_op or Block._prepend_op instead.
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              # var1 += var2 + var3
              cur_block.append_op(type="sum",
                                  inputs={"X": [var1, var2, var3]},
                                  outputs={"Out": [var1]})
      """
      OP_WITHOUT_KERNEL_SET = {
          'feed', 'fetch', 'recurrent', 'go', 'rnn_memory_helper_grad',
          'conditional_block', 'while', 'send', 'recv', 'listen_and_serv',
          'fl_listen_and_serv', 'ncclInit', 'select', 'checkpoint_notify',
          'gen_bkcl_id', 'c_gen_bkcl_id', 'gen_nccl_id', 'c_gen_nccl_id',
          'c_comm_init', 'c_sync_calc_stream', 'c_sync_comm_stream',
          'queue_generator', 'dequeue', 'enqueue', 'heter_listen_and_serv',
          'c_wait_comm', 'c_wait_compute', 'c_gen_hccl_id', 'c_comm_init_hccl',
          'copy_cross_scope'
      }
  
      def __init__(self,
                   block,
                   desc,
                   type=None,
                   inputs=None,
                   outputs=None,
                   attrs=None):
          if in_dygraph_mode():
              if type is None:
                  raise ValueError(
                      "`type` to initialized an Operator can not be None.")
              self._type = type
              self.attrs = attrs if attrs else {}
          else:
              self.block = block
              self.desc = desc
              # note: not add self.attrs here:
              # https://github.com/PaddlePaddle/Paddle/pull/12583#pullrequestreview-145093173
              op_attrs = attrs
              if op_attrs is None:
                  op_attrs = dict()
              del attrs
  
              op_maker = core.op_proto_and_checker_maker
  
              if op_maker.kOpRoleAttrName() not in op_attrs:
                  op_attrs[op_maker.kOpRoleAttrName(
                  )] = self.block.program._op_role
  
              role_var_name = op_maker.kOpRoleVarAttrName()
              if len(self.block.program.
                     _op_role_var) != 0 and role_var_name not in op_attrs:
                  op_attrs[role_var_name] = self.block.program._op_role_var
  
              if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
                  del op_attrs[role_var_name]
  
              if len(self.desc.type()) != 0:
                  return
              if type is None:
                  raise ValueError(
                      "`type` to initialized an Operator can not be None.")
              else:
                  callstack_var_name = op_maker.kOpCreationCallstackAttrName()
                  op_attrs[callstack_var_name] = []
                  for frame in traceback.extract_stack():
                      op_attrs[callstack_var_name].append(
                          '  File "{}", line {}, in {}'.format(frame[0], frame[1],
                                                               frame[2]))
                      op_attrs[callstack_var_name].append('    {}'.format(frame[
                          3]))
  
              self.desc.set_type(type)
              proto = OpProtoHolder.instance().get_op_proto(type)
  
              namescope_var_name = op_maker.kOpNameScopeAttrName()
              op_attrs[namescope_var_name] = _full_name_scope()
  
              # set device for op with kernels, give warning for op without kernels
              # when force_cpu and device_guard are used at the same time, a warning will be given.
              # TODO(zhangting2020): when force_cpu is removed, clear warning below.
              if _current_device is not None:
                  if self._has_kernel(type):
                      op_device = op_maker.kOpDeviceAttrName()
                      op_attrs[op_device] = _current_device
                  else:
                      warnings.warn("The Op(%s) is not support to set device." %
                                    type)
                  if 'force_cpu' in op_attrs:
                      if (type is 'less_than' and op_attrs['force_cpu'] != None
                          ) or op_attrs['force_cpu'] != False:
                          warnings.warn(
                              "The Attr(force_cpu) of Op(%s) will be deprecated in the future, "
                              "please use 'device_guard' instead. 'device_guard' has higher priority when they are "
                              "used at the same time." % type)
  
              def find_name(var_list, name):
                  for var_name in var_list:
                      if var_list[var_name] is not None and var_name == name:
                          return True
                  return False
  
              if inputs is not None:
                  for in_proto in proto.inputs:
                      found = find_name(inputs, in_proto.name)
                      assert found or in_proto.dispensable, "Input {} not found".format(
                          in_proto.name)
                      if found:
                          in_args = inputs[in_proto.name]
                          if not isinstance(in_args, (list, tuple)):
                              in_args = [in_args]
                          if not in_proto.duplicable and len(in_args) > 1:
                              raise ValueError(
                                  "Input %s expects only one input, but %d are given."
                                  % (in_proto.name, len(in_args)))
                          in_arg_names = []
                          for index, arg in enumerate(in_args):
                              if isinstance(arg, six.string_types):
                                  in_arg_names.append(arg)
                              elif isinstance(arg, six.binary_type):
                                  in_arg_names.append(arg.decode())
                              elif isinstance(arg, (Variable, core.VarBase)):
                                  in_arg_names.append(cpt.to_text(arg.name))
                              else:
                                  raise TypeError(
                                      "The type of '%s' in operator %s should be "
                                      "one of [basestring(), str, Varibale] in python2, "
                                      "or one of [str, bytes, Variable] in python3."
                                      "but received : %s" %
                                      (in_proto.name, type, arg))
                          self.desc.set_input(in_proto.name, in_arg_names)
                      else:
                          self.desc.set_input(in_proto.name, [])
  
              if outputs is not None:
                  for m in proto.outputs:
                      if (m.name not in outputs) and m.dispensable:
                          continue
                      if not ((m.name in outputs) or m.dispensable):
                          raise ValueError(("Incorrect setting for output(s) of "
                                            "operator \"%s\", should set: [%s].")
                                           % (type, m.name))
                  for out_proto in proto.outputs:
                      if out_proto.name not in outputs:
                          continue
                      out_args = outputs[out_proto.name]
                      if not isinstance(out_args, list):
                          out_args = [out_args]
                      if not out_proto.duplicable and len(out_args) > 1:
                          raise ValueError(
                              "Output %s expects only one output, but %d are given."
                              % (out_proto.name, len(out_args)))
                      out_arg_names = []
                      for arg in out_args:
                          if isinstance(arg, six.string_types):
                              out_arg_names.append(arg)
                          else:
                              out_arg_names.append(cpt.to_text(arg.name))
                          # TODO(minqiyang): could we remove variable's op in static mode?
                          if not in_dygraph_mode():
                              if isinstance(arg, six.string_types):
                                  block.var(arg).op = self
                              else:
                                  arg.op = self
                      self.desc.set_output(out_proto.name, out_arg_names)
  
              if op_attrs is not None:
                  if not isinstance(op_attrs, dict):
                      raise TypeError("'attrs' should be a dict.")
                  for attr in proto.attrs:
                      attr_name = attr.name
                      if (attr_name not in op_attrs) or (
                              op_attrs[attr_name] is None):
                          continue
                      attr_val = op_attrs[attr_name]
                      self._update_desc_attr(attr_name, attr_val)
  
              self.desc.check_attrs()
              if self._has_kernel(type):
                  self.desc.infer_var_type(self.block.desc)
                  self.desc.infer_shape(self.block.desc)
  
      def _has_kernel(self, op_type):
          return op_type not in self.OP_WITHOUT_KERNEL_SET
  
      def to_string(self, throw_on_error):
          """
          Get debug string.
  
          Args:
              throw_on_error(bool): Whether to raise exception if self is not
                  initialized.
  
          Returns:
              str: The debug string.
  
          """
          protostr = self.desc.serialize_to_string()
          proto = framework_pb2.OpDesc.FromString(six.binary_type(protostr))
          return _debug_string_(proto, throw_on_error)
  
      def _to_readable_code(self, skip_op_callstack=True):
          """
          Get readable debug string of Operator.
  
          .. note::
              If you want to get the debug string in protobuf format,
              please use :code:`to_string` method.
  
          Args:
              skip_op_callstack(bool): whether to skip parsing Operator's attribute
                  op_callstack, default value is True
  
          Returns:
              string: The formatted Operator string.
  
          Examples:
              .. code-block:: python
  
              import paddle.fluid as fluid
  
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              var = cur_block.create_var(name="X",
                                         shape=[-1, 23, 48],
                                         dtype='float32')
              new_op = cur_block.append_op(type="abs",
                                  inputs={"X": [var]},
                                  outputs={"Out": [var]})
              print(new_op._to_readable_code())
          """
          assert isinstance(
              skip_op_callstack, bool
          ), "skip_op_callstack parameter's type is error, expect bool, received %s".format(
              type(skip_op_callstack))
          outputs_str = "{"
          for i in range(0, len(self.output_names)):
              outputs_str += "{name}=".format(name=self.output_names[i])
              o = self.output(self.output_names[i])
              outputs_str += "{value}".format(value=o)
              if i != len(self.output_names) - 1:
                  outputs_str += ", "
          outputs_str += "}"
  
          inputs_str = "{"
          for i in range(0, len(self.input_names)):
              inputs_str += "{name}=".format(name=self.input_names[i])
              o = self.input(self.input_names[i])
              inputs_str += "{value}".format(value=o)
  
              if i != len(self.input_names) - 1:
                  inputs_str += ", "
          inputs_str += "}"
  
          attr_names = sorted(self.attr_names)
          attrs_str = ""
          for i in range(0, len(attr_names)):
              name = attr_names[i]
              if skip_op_callstack and name == "op_callstack":
                  continue
  
              attr_type = self.desc.attr_type(name)
              if attr_type == core.AttrType.BLOCK:
                  a = "{name} = block[{value}]".format(
                      name=name, type=attr_type, value=self._block_attr_id(name))
                  attrs_str += a
                  if i != len(attr_names) - 1:
                      attrs_str += ", "
                  continue
  
              if attr_type == core.AttrType.BLOCKS:
                  a = "{name} = blocks{value}".format(
                      name=name,
                      type=attr_type,
                      value=self._blocks_attr_ids(name))
                  attrs_str += a
                  if i != len(attr_names) - 1:
                      attrs_str += ", "
                  continue
  
              a = "{name} = {value}".format(
                  name=name, type=attr_type, value=self.desc.attr(name))
              attrs_str += a
              if i != len(attr_names) - 1:
                  attrs_str += ", "
  
          if outputs_str != "{}":
              op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\
                  format(outputs=outputs_str, op_type=self.type,
                         inputs=inputs_str, attrs=attrs_str)
          else:
              op_str = "{op_type}(inputs={inputs}, {attrs})".\
                  format(op_type=self.type, inputs=inputs_str, attrs=attrs_str)
          return op_str
  
      def __str__(self):
          return self._to_readable_code()
  
      __repr__ = __str__
  
      @property
      def type(self):
          return self.desc.type()
  
      def input(self, name):
          r"""
          Get the input arguments according to the input parameter name.
  
          Args:
              name(str): The input parameter name.
  
          Returns:
              list: return the list of argument names that associated with \
                  the specific parameter name.
          """
          return self.desc.input(name)
  
      def _rename_input(self, old_name, new_name):
          """
          Rename the `old_name` to `new_name`.
  
          Args:
              old_name(str): The old name of the Operator's input.
              new_name(str): The new name of the Operator's input.
  
          Returns:
              None
          """
          self.desc._rename_input(old_name, new_name)
  
      def _rename_output(self, old_name, new_name):
          """
          Rename the `old_name` to `new_name`.
  
          Args:
              old_name(str): The old name of the Operator's output.
              new_name(str): The new name of the Operator's output.
  
          Returns:
              None
          """
          self.desc._rename_output(old_name, new_name)
  
      @property
      def input_names(self):
          return self.desc.input_names()
  
      @property
      def input_arg_names(self):
          return self.desc.input_arg_names()
  
      @property
      def output_arg_names(self):
          return self.desc.output_arg_names()
  
      def output(self, name):
          r"""
          Get output arguments by the output parameter name.
  
          Args:
              name(str): The output parameter name.
  
          Returns:
              list: return the list of argument names associated with \
                  the specific parameter name.
          """
          return self.desc.output(name)
  
      @property
      def output_names(self):
          return self.desc.output_names()
  
      @property
      def idx(self):
          for i, op in enumerate(self.block.ops):
              if op == self:
                  return i
          raise ValueError(
              "Can't find op itself in it's block. It could be a bug of Paddle.")
  
      def has_attr(self, name):
          """
          Whether this Operator has the attribute with name or not.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              bool: True if has this attribute.
  
          """
          return self.desc.has_attr(name)
  
      def attr_type(self, name):
          """
          Get the type of attribute by attribute's name.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              core.AttrType: the attribute type.
          """
          return self.desc.attr_type(name)
  
      def _set_attr(self, name, val):
          """
          Set the value of attribute by attribute's name.
  
          Args:
              name(str): the attribute name.
              val(bool|int|str|float|list): the value of the attribute.
  
          Raises:
              ValueError: If the type of value doesn't match with desc.attr_type(name).
          """
          self._update_desc_attr(name, val)
  
      def _remove_attr(self, name):
          self.desc.remove_attr(name)
  
      def _update_desc_attr(self, name, val):
          """
          Update the value of desc's attribute by attribute's name.
  
          Args:
              name(str): the attribute name.
              val(bool|int|str|float|list): the value of the attribute.
  
          Raises:
              ValueError: If the type of value doesn't match with desc.attr_type(name).
          """
          if isinstance(val, Block):
              self.desc.set_block_attr(name, val.desc)
          elif isinstance(val, list) and val and all(
                  isinstance(v, Block) for v in val):
              self.desc.set_blocks_attr(name, [v.desc for v in val])
          elif isinstance(val, core.BlockDesc) or \
                  isinstance(val, core.ProgramDesc):
              self.desc.set_serialized_attr(name, val.serialize_to_string())
          else:
              self.desc._set_attr(name, val)
  
      @property
      def attr_names(self):
          return self.desc.attr_names()
  
      def attr(self, name):
          """
          Get the attribute by name.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              bool|int|str|float|list: The attribute value. The return value
              can be any valid attribute type.
          """
          return self.desc.attr(name)
  
      def _block_attr_id(self, name):
          """
          Get the block attribute's id by name.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              int: the block index.
          """
          return self.desc._block_attr_id(name)
  
      def _block_attr(self, name):
          """
          Get the block attribute  by name.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              block: the block attribute.
          """
  
          id = self._block_attr_id(name)
          assert (id >= 0 and id < len(self.block.program.blocks))
          return self.block.program.blocks[id]
  
      def _blocks_attr(self, name):
          """
          Get the blocks attribute  by name.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              list: list of the blocks attribute.
          """
          attrs = []
          for i in self._blocks_attr_ids(name):
              assert (i >= 0 and i < len(self.block.program.blocks))
              attrs.append(self.block.program.blocks[i])
  
          return attrs
  
      def _blocks_attr_ids(self, name):
          """
          Get the blocks attribute's ids by name.
  
          Args:
              name(str): the attribute name.
  
          Returns:
              list: list of the blocks ids.
          """
  
          return self.desc._blocks_attr_ids(name)
  
      def all_attrs(self):
          """
          Get the attribute dict.
  
          Returns:
              dict: The Operator's attribute dict, name->attr.
          """
          attr_names = self.attr_names
          attr_map = {}
          for n in attr_names:
              attr_type = self.desc.attr_type(n)
              if attr_type == core.AttrType.BLOCK:
                  attr_map[n] = self._block_attr(n)
                  continue
  
              if attr_type == core.AttrType.BLOCKS:
                  attr_map[n] = self._blocks_attr(n)
                  continue
  
              attr_map[n] = self.attr(n)
  
          return attr_map
  
      def _is_optimize_op(self):
          op_maker = core.op_proto_and_checker_maker
          OPTIMIZE = core.op_proto_and_checker_maker.OpRole.Optimize
  
          if not self.desc.has_attr(op_maker.kOpRoleAttrName()):
              return False
  
          op_role = self.desc.attr(op_maker.kOpRoleAttrName())
          if op_role & int(OPTIMIZE):
              return True
  
          return False
  
      def _is_backward_op(self):
          op_maker = core.op_proto_and_checker_maker
          BACKWARD = core.op_proto_and_checker_maker.OpRole.Backward
  
          if not self.desc.has_attr(op_maker.kOpRoleAttrName()):
              return False
  
          op_role = self.desc.attr(op_maker.kOpRoleAttrName())
          if op_role & int(BACKWARD):
              return True
  
          return False
  
  
  class Block(object):
      """
      In Fluid, a Program is consistence of multi-Block, and Block stores
      VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name.
      One block could have some child blocks, and child block's name scopes
      should inherit the parent's so that OpDesc in child block can reference
      a VarDesc that is stored in the parent block.
      Please reference the framework.proto for details.
  
      Args:
          program(Program): The Program that the Block belongs to.
          idx(int): The block's id in the Program.
  
      Notes:
          The constructor of Block should not be invoked directly. Please
          use `Program._create_block()` to create a block.
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
  
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              var = cur_block.create_var(name="X",
                                         shape=[-1, 23, 48],
                                         dtype='float32')
              cur_block.append_op(type="abs",
                                  inputs={"X": [var]},
                                  outputs={"Out": [var]})
      """
  
      def __init__(self, program, idx):
          self.desc = program.desc.block(idx)
          self.vars = collections.OrderedDict()  # var_name --> var
          self.ops = list()  # operator list
          self.program = program
          self.removed_vars = collections.OrderedDict()
  
      def __str__(self):
          return self._to_readable_code()
  
      def _to_readable_code(self, skip_op_callstack=True):
          """
          Get readable debug string of Block.
  
          .. note::
              If you want to get the debug string in protobuf format,
              please use :code:`to_string` method.
  
          Args:
              skip_op_callstack(bool): whether to skip parsing Operator's attribute
                  op_callstack, default value is True
  
          Returns:
              string: The formatted Block string.
  
          Examples:
              .. code-block:: python
  
              import paddle.fluid as fluid
  
              cur_program = fluid.Program()
              cur_block = cur_program.current_block()
              new_var = cur_block.create_var(name="X",
                                             shape=[-1, 23, 48],
                                             dtype='float32')
              new_op = cur_block.append_op(type="abs",
                                  inputs={"X": [new_var]},
                                  outputs={"Out": [new_var]})
              print(cur_block._to_readable_code())
          """
          assert isinstance(
              skip_op_callstack, bool
          ), "skip_op_callstack parameter's type is error, expect bool, received %s".format(
              type(skip_op_callstack))
          block_str = "{ // block "
          block_str += "{}\n".format(self.idx)
          for var in list(self.vars.values()):
              block_str += "    {}\n".format(var._to_readable_code())
          block_str += "\n"
          for op in self.ops:
              block_str += "    {}\n".format(
                  op._to_readable_code(skip_op_callstack))
          block_str += "}"
          return block_str
  
      def to_string(self, throw_on_error, with_details=False):
          """
          Get debug string.
  
          Args:
              throw_on_error(bool): raise exception when self is not initialized
                  when throw_on_error is True.
              with_details(bool): more details about variables and parameters
                  (e.g. trainable, optimize_attr, ...) will be printed when
                  with_details is True. Default False.
  
          Returns:
              str: The debug string.
          """
          assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                 bool)
          if with_details:
              re_add_indent = re.compile(r"\n(.)")
              res_str = "blocks {\n  idx: %d\n  parent_idx: %d" % (
                  self.idx, self.parent_idx)
              for var in list(self.vars.values()):
                  res_str += "\n  vars {\n    %s  }" % re_add_indent.sub(
                      r"\n    \1", var.to_string(throw_on_error, with_details))
              for op in self.ops:
                  res_str += "\n  ops {\n    %s  }" % re_add_indent.sub(
                      r"\n    \1", op.to_string(throw_on_error))
              res_str += "\n}"
          else:
              protostr = self.desc.serialize_to_string()
              proto = framework_pb2.BlockDesc.FromString(
                  six.binary_type(protostr))
              res_str = _debug_string_(proto, throw_on_error)
          return res_str
  
      __repr__ = __str__
  
      @property
      def parent_idx(self):
          return self.desc.parent
  
      @property
      def forward_block_idx(self):
          return self.desc.get_forward_block_idx()
  
      def _set_forward_block_idx(self, idx):
          """
          Set the forward block Idx.
  
          Args:
              idx(int): the block index.
  
          Returns:
              None
          """
          self.desc._set_forward_block_idx(idx)
  
      @property
      def backward_block_idx(self):
          cur_block_idx = self.idx
          for block in self.program.blocks:
              if block.forward_block_idx == cur_block_idx:
                  return block.idx
          return -1
  
      @property
      def idx(self):
          return self.desc.id
  
      def var(self, name):
          """
          Get a Variable by name from this block.
  
          Args:
              name(str): the Variable's name.
  
          Raises:
              ValueError: The If input's type is not str, or this block
                  doesn't have a Variable with the giving name.
  
          Returns:
              Variable: the Variable with the giving name.
          """
          if not isinstance(name, six.string_types):
              raise TypeError(
                  "var require string as parameter, but get %s instead." %
                  (type(name)))
          v = self.vars.get(name, None)
          if v is None:
              raise ValueError("var %s not in this block" % name)
          return v
  
      def _find_var_recursive(self, name):
          """
          Get a Variable by name from this block recursively.
  
          Args:
              name(str): the Variable's name.
  
          Returns:
              Variable: the Variable with the giving name. Or None if not found.
          """
          frontier = list()
          visited = set()
  
          frontier.append(self)
  
          prog = self.program
  
          while len(frontier) != 0:  # BFS
              cur = frontier[0]
              frontier = frontier[1:]
  
              if id(cur) in visited:
                  continue
  
              if cur.has_var(name):
                  return cur.var(name)
  
              if cur.parent_idx != -1:
                  frontier.append(prog.block(cur.parent_idx))
  
              if cur.forward_block_idx != -1:
                  frontier.append(prog.block(cur.forward_block_idx))
  
              visited.add(id(cur))
          return None
  
      def _var_recursive(self, name):
          """
          Get a Variable by name from this block recursively.
  
          Args:
              name(str): the Variable's name.
  
          Raises:
              ValueError: this block and this parent block doesn't
                  have a Variable with the giving name.
  
          Returns:
              Variable: the Variable with the giving name.
          """
          var = self._find_var_recursive(name)
          if var:
              return var
          else:
              raise ValueError("Var {0} is not found recursively".format(name))
  
      def all_parameters(self):
          return list(self.iter_parameters())
  
      def iter_parameters(self):
          return (item[1] for item in six.iteritems(self.vars)
                  if isinstance(item[1], Parameter))
  
      def create_var(self, *args, **kwargs):
          if in_dygraph_mode():
              var = _varbase_creator(*args, **kwargs)
          else:
              var = Variable(block=self, *args, **kwargs)
              if 'initializer' in kwargs:
                  kwargs['initializer'](var, self)
          return var
  
      def has_var(self, name):
          return name in self.vars
  
      def _rename_var(self, name, new_name):
          """
          Rename variable in vars and ops' inputs and outputs
  
          Args:
              name(str): the name that need to be renamed.
              new_name(str): the name that need to rename to.
  
          Raises:
              ValueError: If this block doesn't have this the giving name,
                  or the type of the var with the giving name is not Parameter
                  or Variable.
  
          Returns:
              Variable: the Variable with the giving name.
          """
          name = cpt.to_text(name)
          new_name = cpt.to_text(new_name)
  
          if not self.has_var(name):
              raise ValueError("var %s is not in current block" % name)
          v = self.var(name)
          if type(v) == Parameter:
              var_type = "Parameter"
              stop_gradient = v.stop_gradient
              trainable = v.trainable
              optimize_attr = v.optimize_attr
              regularizer = v.regularizer
              error_clip = v.error_clip
          elif type(v) == Variable:
              var_type = "Variable"
              error_clip = v.error_clip
              stop_gradient = v.stop_gradient
          else:
              raise ValueError("unsupported var type: %s", type(v))
          orig_var_type = v.type
          self.desc._rename_var(cpt.to_bytes(name), cpt.to_bytes(new_name))
          # NOTE: v is destroyed by C++ after calling _rename_var.
          d = self.desc.find_var(cpt.to_bytes(new_name))
          if var_type == "Parameter":
              if in_dygraph_mode():
                  var = ParamBase(
                      d.shape(),
                      d.dtype(),
                      type=orig_var_type,
                      name=new_name,
                      stop_gradient=stop_gradient,
                      trainable=trainable,
                      optimize_attr=optimize_attr,
                      regularizer=regularizer,
                      error_clip=error_clip)
              else:
                  var = Parameter(
                      self,
                      d.shape(),
                      d.dtype(),
                      type=orig_var_type,
                      name=new_name,
                      stop_gradient=stop_gradient,
                      trainable=trainable,
                      optimize_attr=optimize_attr,
                      regularizer=regularizer,
                      error_clip=error_clip)
          elif var_type == "Variable":
              var = Variable(
                  self,
                  type=orig_var_type,
                  name=new_name,
                  error_clip=error_clip,
                  stop_gradient=stop_gradient)
  
          # rename the python side, _sync_with_cpp will only add
          # new vars/ops to python side.
          self.vars[new_name] = var
          del self.vars[name]
          self._sync_with_cpp()
          return var
  
      def _remove_var(self, name, sync=True):
          if sync == True:
              self._sync_with_cpp()
          self.desc._remove_var(cpt.to_bytes(name))
          del self.vars[name]
  
      def create_parameter(self, *args, **kwargs):
          global_block = self.program.global_block()
          param = None
          if in_dygraph_mode():
              param = ParamBase(*args, **kwargs)
          else:
              param = Parameter(global_block, *args, **kwargs)
              # NOTE: Why only set stop_gradient=False in static mode
              # Because in dygraph mode, the `stop_gradient` and `trainable`
              # are related, and `trainable` default vallue is `True` or
              # it is specified by users, there is no need to set
              # `stop_gradient` for ParamBase here.
              param.stop_gradient = False
          if 'initializer' in kwargs:
  
              def _is_inited_by(block, var):
                  init_ops = []
                  for op in block.ops:
                      if var.name in op.output_arg_names:
                          # In startup_program, "c_broadcast" and "c_sync_comm_stream"
                          # are treated as initialization ops that cause error.
                          # Think of "c_broadcast" and "c_sync_comm_stream" as a special case here.
                          # NOTE: "coalesce_tensor" is a special case for rnn with cudnn support
                          if op.type in [
                                  "c_broadcast", "c_sync_comm_stream",
                                  "coalesce_tensor"
                          ]:
                              continue
                          init_ops.append(op)
                  return init_ops
  
              initializer = kwargs['initializer']
              init_ops = _is_inited_by(global_block, param)
              init_ops_len = len(init_ops)
              if init_ops_len > 1:
                  raise RuntimeError("param " + param.name +
                                     " is inited by multiple init ops " + str(
                                         init_ops))
              elif init_ops_len == 1:
                  # TODO already inited, do nothing, should log a warning
                  pass
              else:
                  initializer(param, self)
          return param
  
      def append_op(self, *args, **kwargs):
          """
          Appends a new Operator according to the giving arguments.
  
          Returns:
              Operator: the append Operator.
          """
          if in_dygraph_mode():
              attrs = kwargs.get("attrs", {})
              type = kwargs.get("type", None)
              op = Operator(
                  block=self,
                  desc=None,
                  type=type,
                  inputs=None,
                  outputs=None,
                  attrs=attrs)
  
              # record ops in tracer rather than blocks
              #
              # TODO(minqiyang): add op stop_gradient support in static mode too.
              # currently, we only support stop_gradient in dygraph mode.
  
              _dygraph_tracer().trace_op(type,
                                         kwargs.get("inputs", {}),
                                         kwargs.get("outputs", {}), attrs
                                         if attrs else {},
                                         kwargs.get("stop_gradient", False))
          else:
              op_desc = self.desc.append_op()
              op = Operator(
                  block=self,
                  desc=op_desc,
                  type=kwargs.get("type", None),
                  inputs=kwargs.get("inputs", None),
                  outputs=kwargs.get("outputs", None),
                  attrs=kwargs.get("attrs", None))
  
              self.ops.append(op)
  
          return op
  
      def _insert_op(self, index, *args, **kwargs):
          """
          Insert a Operator according to the giving arguments.
  
          Args:
              index(int): the place that the operator to insert.
  
          Returns:
              Operator: the insert Operator.
          """
          self._sync_with_cpp()
          return self._insert_op_without_sync(index, *args, **kwargs)
  
      def _insert_op_without_sync(self, index, *args, **kwargs):
          """
          Insert an Operator according to the giving arguments, 
          without sync_with_cpp to meke the compilation faster.
  
          Args:
              index(int): the place that the operator to insert.
  
          Returns:
              Operator: the insert Operator.
          """
          op_desc = self.desc._insert_op(index)
          op = Operator(block=self, desc=op_desc, *args, **kwargs)
          self.ops.insert(index, op)
          return op
  
      def _remove_op(self, index, sync=True):
          """
          Remove the specific position operator.
  
          Args:
              index(int): the position that the operator to insert.
  
          Returns:
              None
          """
          if sync == True:
              self._sync_with_cpp()
          self.desc._remove_op(index, index + 1)
          del self.ops[index]
  
      def _slice_ops(self, start, end):
          """
          Return the Operator between start and end.
  
          Args:
              start(int): the start position.
              end(int): the end position.
  
          Returns:
              list: the Operators between start and end.
          """
          return self.ops[start:end]
  
      def _prepend_op(self, *args, **kwargs):
          if in_dygraph_mode():
              type = kwargs.get("type", None)
              attrs = kwargs.get("attrs", {})
              op = Operator(
                  self, None, type=type, inputs=None, outputs=None, attrs=attrs)
  
              _dygraph_tracer().trace_op(type,
                                         kwargs.get("inputs", {}),
                                         kwargs.get("outputs", {}), attrs
                                         if attrs else {},
                                         kwargs.get("stop_gradient", False))
          else:
              op_desc = self.desc._prepend_op()
              op = Operator(
                  self,
                  op_desc,
                  type=kwargs.get("type", None),
                  inputs=kwargs.get("inputs", None),
                  outputs=kwargs.get("outputs", None),
                  attrs=kwargs.get("attrs", None))
              self.ops.insert(0, op)
  
          return op
  
      def _sync_with_cpp(self):
          """
          Sync from the desc on the c++ end. This method is used to synchronize
          the c++ desc instance generated by backward.
          """
          # sync variables from cpp
          for var in self.desc.all_vars():
              if not self.has_var(var.name()):
                  self.create_var(name=var.name(), desc=var, type=var.type())
  
          # sync variables removed from c++ end
          for var in list(self.vars.keys()):
              if not self.desc.find_var(cpt.to_bytes(var)):
                  self.vars.pop(var)
  
          # sync operators from cpp
          ops_in_cpp = []
          for op_idx in range(0, self.desc.op_size()):
              ops_in_cpp.append(self.desc.op(op_idx))
  
          if len(self.ops) != 0:
              first_op_in_python = self.ops[0].desc
              last_op_in_python = self.ops[len(self.ops) - 1].desc
              start_index = None
              end_index = None
              for index in range(len(ops_in_cpp)):
                  if first_op_in_python == ops_in_cpp[index]:
                      start_index = index
                  if last_op_in_python == ops_in_cpp[index]:
                      end_index = index
              assert start_index is not None
              assert end_index is not None
              assert start_index <= end_index
          else:
              start_index = 0
              end_index = -1
  
          # sync ops append to the head of cpp_ops
          for index in range((start_index - 1 - 1), -1, -1):
              op_desc = ops_in_cpp[index]
              op = Operator(self, op_desc)
              self.ops.insert(0, op)
  
          # sync ops append to the end of cpp_ops
          for index in range((end_index + 1), len(ops_in_cpp)):
              op_desc = ops_in_cpp[index]
              op = Operator(self, op_desc)
              self.ops.append(op)
  
          # sync ops removed from c++ end
          if end_index != -1 and end_index < len(self.ops):
              ops_in_cpp_index = 0
              ops_in_python_index = 0
              while ops_in_python_index < len(
                      self.ops) and ops_in_cpp_index < len(ops_in_cpp):
                  if self.ops[ops_in_python_index].desc != ops_in_cpp[
                          ops_in_cpp_index]:
                      del self.ops[ops_in_python_index]
                  else:
                      ops_in_cpp_index += 1
                      ops_in_python_index += 1
  
          assert len(self.ops) == len(ops_in_cpp)
          for index in range(len(self.ops)):
              assert self.ops[index].desc == ops_in_cpp[index]
  
      def _copy_param_info_from(self, other):
          """
          Copy the information of parameters from the other block.
  
          Args:
              other(Block): the other block.
  
          Raises:
              ValueError: If type of input is not Block, or the `other` and this
                  block is not in the same topology.
  
          Returns:
              None
          """
          if not isinstance(other, Block):
              raise TypeError(
                  "_copy_param_info_from should be invoked with Block")
          for p in other.iter_parameters():
              assert isinstance(p, Parameter)
              v = self.vars.get(p.name, None)
              if v is None:
                  # if the Parameter is pruned, v may be None
                  continue
              assert isinstance(v, Variable)
              new_p = None
              if in_dygraph_mode():
                  new_p = ParamBase(
                      shape=v.shape,
                      dtype=v.dtype,
                      type=v.type,
                      lod_level=v.lod_level,
                      stop_gradient=p.stop_gradient,
                      trainable=p.trainable,
                      optimize_attr=p.optimize_attr,
                      regularizer=p.regularizer,
                      error_clip=p.error_clip,
                      name=v.name)
              else:
                  new_p = Parameter(
                      block=self,
                      shape=v.shape,
                      dtype=v.dtype,
                      type=v.type,
                      lod_level=v.lod_level
                      if v.type == core.VarDesc.VarType.LOD_TENSOR else None,
                      stop_gradient=p.stop_gradient,
                      trainable=p.trainable,
                      optimize_attr=p.optimize_attr,
                      regularizer=p.regularizer,
                      error_clip=p.error_clip,
                      name=v.name)
              self.vars[new_p.name] = new_p
  
      def _clone_variable(self, var, force_persistable=True):
          """
          Clone a variable into current block.
  
          Args:
              var: the variable to be cloned.
              force_persistable(bool): True means setting the result variable to being persistable.
                                       False means setting the persistable the same with that of input var.
                                       default: True.
  
          Returns:
              Variable: the new  variable cloned from 'var' in current block.
          """
          assert isinstance(var, Variable)
          ret_var = None
          # make STEP_SCOPES var can be safely cloned.
          if var.type == core.VarDesc.VarType.STEP_SCOPES:
              ret_var = self.create_var(
                  name=var.name, persistable=var.persistable, type=var.type)
          elif var.type == core.VarDesc.VarType.RAW:
              ret_var = self.create_var(
                  name=var.name, persistable=var.persistable, type=var.type)
          elif var.type == core.VarDesc.VarType.SELECTED_ROWS:
              ret_var = self.create_var(
                  name=var.name,
                  shape=var.shape,
                  dtype=var.dtype,
                  type=var.type,
                  persistable=True if force_persistable else var.persistable,
                  is_data=var.is_data,
                  need_check_feed=var.desc.need_check_feed())
          else:
              ret_var = self.create_var(
                  name=var.name,
                  shape=var.shape,
                  dtype=var.dtype,
                  type=var.type,
                  lod_level=var.lod_level,
                  persistable=True if force_persistable else var.persistable,
                  is_data=var.is_data,
                  need_check_feed=var.desc.need_check_feed())
          return ret_var
  
  
  class IrNode(object):
      """
      Python IrNode. Beneath it is a core.Node, which is used for Ir Pass.
      """
  
      def __init__(self, node):
          """
          Construct an IrNode using core.Node.
  
          Args:
              node(core.Node): C++ Node.
          """
          assert isinstance(node,
                            core.Node), 'node must be the instance of core.Node.'
          self.node = node
  
      def name(self):
          """
          Return the node name.
  
          Returns:
              str: node name.
          """
          return self.node.name()
  
      def node_type(self):
          """
          Return the node type.
  
          Returns:
              core.Node.Type: node type(core.Node.Type.Operation or core.Node.Type.Variable).
          """
          return self.node.node_type()
  
      def var(self):
          """
          Return the node variable description.
  
          Returns:
              core.VarDesc: node variable description.
          """
          return self.node.var()
  
      def op(self):
          """
          Return the node operator description.
  
          Returns:
              core.OpDesc: node operator description.
          """
          return self.node.op()
  
      def id(self):
          """
          Return the node id.
  
          Returns:
              int: node id.
          """
          return self.node.id()
  
      def is_op(self):
          """
          If the node is an operator, then return true.
  
          Returns:
              bool: indicate whether the node is an operator.
          """
          return self.node.is_op()
  
      def is_var(self):
          """
          If the node is a variable, then return true.
  
          Returns:
              bool: indicate whether the node is a variable.
          """
          return self.node.is_var()
  
      def is_ctrl_var(self):
          """
          If the node is a control dependence variable, then return true.
  
          Returns:
              bool: indicate whether the node is a control dependence variable.
          """
          return self.node.is_ctrl_var()
  
      def clear_inputs(self):
          """
          Clear the node inputs. After executing the `clear_inputs` function,
          the node inputs will be empty.
          """
          self.node.clear_inputs()
  
      def remove_input_by_id(self, node_id):
          """
          Remove a node from inputs by the given node id.
  
          Args:
              node_id(int): the given node id.
          """
          self.node.remove_input(node_id)
  
      def remove_input(self, node):
          """
          Remove a node from inputs.
  
          Args:
              node(IrNode): the node being removed.
          """
          self.node.remove_input(node.node)
  
      def append_input(self, node):
          """
          Append a node in inputs.
  
          Args:
              node(IrNode): the node being appended.
          """
          self.node.append_input(node.node)
  
      def clear_outputs(self):
          """
          Clear the node outputs. After executing the `clear_outputs` function,
          the node outputs will be empty.
          """
          self.node.clear_outputs()
  
      def remove_output_by_id(self, node_id):
          """
          Remove a node from outputs by the given node id.
  
          Args:
              node_id(int): the given node id.
          """
          self.node.remove_output(node_id)
  
      def remove_output(self, node):
          """
          Remove a node from outputs.
  
          Args:
              node(IrNode): the node being removed.
          """
          self.node.remove_output(node.node)
  
      def append_output(self, node):
          """
          Append a node in outputs.
  
          Args:
              node(IrNode): the node being appended.
          """
          self.node.append_output(node.node)
  
      @property
      def inputs(self):
          """
          Return the node inputs.
  
          Returns:
              list(IrNode): node inputs wrapped by IrNode.
          """
          return [IrNode(n) for n in self.node.inputs]
  
      @property
      def outputs(self):
          """
          Return the node outputs.
  
          Returns:
              list(IrNode): node outputs wrapped by IrNode.
          """
          return [IrNode(n) for n in self.node.outputs]
  
  
  class IrVarNode(IrNode):
      """
      Python IrVarNode. Beneath it is a core.Node, it inherits from IrNode.
      """
  
      def __init__(self, node):
          """
          Construct an IrVarNode using core.Node.
  
          Args:
              node(core.Node): C++ Node.
          """
          assert isinstance(node, core.Node) and node.is_var(), \
              'node must be the instance of core.Node and it must be a variable node.'
          super(IrVarNode, self).__init__(node)
          self.node = node
  
      def set_shape(self, shape):
          """
          Set the node variable shape.
  
          Args:
              shape(list): shape to be set.
          """
          assert self.node.var() is not None, \
              "The node variable description can not be None."
          self.node.var().set_shape(shape)
  
      def persistable(self):
          """
          If the variable node is a persistable variable, then return true.
  
          Returns:
              bool: indicate whether the variable is persistable.
          """
          assert self.node.var() is not None, \
              "The node variable description can not be None."
          return self.node.var().persistable()
  
      def type(self):
          """
          Return the variable type.
  
          Returns:
              core.VarDesc.VarType: the variable type.
          """
          assert self.node.var() is not None, \
              "The node variable description can not be None."
          return self.node.var().type()
  
      def dtype(self):
          """
          Return the variable data type.
  
          Returns:
              core.VarDesc.VarType: the variable data type.
          """
          assert self.node.var() is not None, \
              "The node variable description can not be None."
          return self.node.var().dtype()
  
      def shape(self):
          """
          Return the variable shape.
  
          Returns:
              list: the variable shape.
          """
          assert self.node.var() is not None, \
              "The node variable description can not be None."
          return self.node.var().shape()
  
      @property
      def inputs(self):
          """
          Return the node inputs.
  
          Returns:
              list(IrOpNode): node inputs wrapped by IrOpNode.
          """
          return [IrOpNode(n) for n in self.node.inputs]
  
      @property
      def outputs(self):
          """
          Return the node outputs.
  
          Returns:
              list(IrOpNode): node outputs wrapped by IrOpNode.
          """
          return [IrOpNode(n) for n in self.node.outputs]
  
  
  class IrOpNode(IrNode):
      """
      Python IrOpNode. Beneath it is a core.Node, it inherits from IrNode.
      """
  
      def __init__(self, node):
          """
          Construct an IrOpNode using core.Node.
  
          Args:
              node(core.Node): C++ Node.
          """
          assert isinstance(node, core.Node) and node.is_op(), \
              'node must be the instance of core.Node and it must be a operator node.'
          super(IrOpNode, self).__init__(node)
          self.node = node
  
      def rename_input(self, old_input_name, new_input_name):
          """
          Rename the input of this node.
  
          Args:
              old_input_name(str): the old input name.
              new_input_name(str): the new input name.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          self.node.op()._rename_input(old_input_name, new_input_name)
  
      def rename_output(self, old_output_name, new_output_name):
          """
          Rename the output of this node.
  
          Args:
              old_output_name(str): the old output name.
              new_output_name(str): the new output name.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          self.node.op()._rename_output(old_output_name, new_output_name)
  
      def input(self, name):
          """
          Get the argument name list by the parameter name for input.
  
          Args:
              name(str): the parameter name.
  
          Returns:
              list(str): the argument name list.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          return self.node.op().input(name)
  
      def output(self, name):
          """
          Get the argument name list by the parameter name for output.
  
          Args:
              name(str): the parameter name.
  
          Returns:
              list(str): the argument name list.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          return self.node.op().output(name)
  
      def set_type(self, new_type):
          """
          Change the operator type into new type.
  
          Args:
              new_type(str): new operator type to be set.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          return self.node.op().set_type(new_type)
  
      def set_attr(self, name, val):
          """
          Set the value of attribute by attribute's name.
  
          Args:
              name(str): the attribute name.
              val(bool|int|str|float|list): the value of the attribute.
          """
          self._update_desc_attr(name, val)
  
      def _update_desc_attr(self, name, val):
          """
          Update the value of the op desc's attribute by attribute's name.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          desc = self.node.op()
          if isinstance(val, Block):
              desc.set_block_attr(name, val.desc)
          elif isinstance(val, list) and val and \
                  all(isinstance(v, Block) for v in val):
              desc.set_blocks_attr(name, [v.desc for v in val])
          elif isinstance(val, core.BlockDesc) or \
                  isinstance(val, core.ProgramDesc):
              desc.set_serialized_attr(name, val.serialize_to_string())
          else:
              desc._set_attr(name, val)
  
      def input_arg_names(self):
          """
          Return input arguments' names of this op node.
  
          Returns:
              list(str): input arguments' names of this op node.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          return self.node.op().input_arg_names()
  
      def output_arg_names(self):
          """
          Return output arguments' names of this op node.
  
          Returns:
              list(str): output arguments' names of this op node.
          """
          assert self.node.op() is not None, \
              "The node operator description can not be None."
          return self.node.op().output_arg_names()
  
      @property
      def inputs(self):
          """
          Return the node inputs.
  
          Returns:
              list(IrVarNode): node inputs wrapped by IrVarNode.
          """
          return [IrVarNode(n) for n in self.node.inputs]
  
      @property
      def outputs(self):
          """
          Return the node outputs.
  
          Returns:
              list(IrVarNode): node outputs wrapped by IrVarNode.
          """
          return [IrVarNode(n) for n in self.node.outputs]
  
  
  class IrGraph(object):
      """
      Python IrGraph. Beneath it is a core.Graph, which is used for
      creating a c++ Ir Pass Graph. An IrGraph is just a graph view of
      a Program. In an IrGraph, both Variables and Operators are graph
      nodes.
      """
  
      def __init__(self, graph, for_test=False):
          """
          Construct an IrGraph using core.Graph.
  
          Args:
              graph(core.Graph): C++ Graph.
              for_test(bool): True for the test graph and false for the train graph.
          """
          assert isinstance(
              graph, core.Graph), 'graph must be the instance of core.Graph.'
          self.graph = graph
          self._for_test = for_test
  
      def clone(self):
          """
          Create a new and duplicated IrGraph.
  
          Warns:
              The method only clones the graph structure, not its attributes.
  
          Returns:
              IrGraph: A new and duplicated graph.
          """
          g = self.graph.clone()
          return IrGraph(g, self._for_test)
  
      def is_test(self):
          """
          If the graph is used for testing, the function returns true. Otherwise, returns false.
          """
          return self._for_test
  
      def all_nodes(self):
          """
          Return all nodes included in the graph as a set.
          """
          return {IrNode(node) for node in self.graph.nodes()}
  
      def all_var_nodes(self):
          """
          Return all variable nodes included in the graph as a set.
          """
          return {IrVarNode(node) for node in self.graph.nodes() if node.is_var()}
  
      def all_persistable_nodes(self):
          """
          Return all persistable variable nodes included in the graph as a set.
          """
          persistable_nodes = set()
          for node in self.graph.nodes():
              if node.is_var() and node.var() is not None and node.var(
              ).persistable():
                  persistable_nodes.add(node)
          return {IrVarNode(p) for p in persistable_nodes}
  
      def all_op_nodes(self):
          """
          Return all operator nodes included in the graph as a set.
          """
          return {IrOpNode(node) for node in self.graph.nodes() if node.is_op()}
  
      def create_persistable_node(self, name, var_type, shape, var_dtype):
          """
          Create a persistable variable node in the graph. In IrGraph,
          it can not distinguish between persistable variables and parameters.
  
          Args:
              name(str): the name of the persistable variable node.
              vart_type(core.VarDesc.VarType): the type of the persistable variable node.
              shape(list): the shape of the persistable variable node.
              var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.
  
          Returns:
              IrVarNode: the created persistable variable node.
          """
          var_desc = core.VarDesc(name)
          var_desc.set_type(var_type)
          var_desc.set_shape(shape)
          var_desc.set_dtype(var_dtype)
          var_desc.set_persistable(True)
          return IrVarNode(self.graph.create_var_node(var_desc))
  
      def create_var_node(self, name, var_type, shape, var_dtype):
          """
          Create a variable node in the graph. The created variable node is
          not persistable.
  
          Args:
              name(str): the name of the variable node.
              vart_type(core.VarDesc.VarType): the type of the variable node.
              shape(list): the shape of the variable node.
              var_dtype(core.VarDesc.VarType): the data type of the variable node.
  
          Returns:
              IrVarNode: the created variable node.
          """
  
          var_desc = core.VarDesc(name)
          var_desc.set_type(var_type)
          var_desc.set_shape(shape)
          var_desc.set_dtype(var_dtype)
          return IrVarNode(self.graph.create_var_node(var_desc))
  
      def create_control_dep_var(self):
          """
          create a control var
          """
          return IrVarNode(self.graph.create_control_dep_var())
  
      def create_var_node_from_desc(self, var_desc):
          """
          Create a variable node by using an existing VarDesc in the graph.
          Depend on the giving VarDesc, the created variable node may be persistable.
  
          Args:
              var_desc(core.VarDesc): the giving variable description.
  
          Returns:
              IrVarNode: the created variable node.
          """
          return IrVarNode(self.graph.create_var_node(var_desc))
  
      def create_op_node(self, op_type, attrs, inputs, outputs):
          """
          Create a operator node in the graph.
  
          Args:
              op_type(str): the type of the operator node.
              attrs(dict): the attributes of the operator node.
              inputs(dict): the inputs of the operator node.
              outputs(dict): the outputs of the operator node.
  
          Returns:
              IrOpNode: the created operator node.
          """
          op_desc = core.OpDesc()
          op_desc.set_type(op_type)
          for attr, value in six.iteritems(attrs):
              self._update_desc_attr(op_desc, attr, value)
          for input_name, var_nodes in six.iteritems(inputs):
              if not isinstance(var_nodes, list):
                  var_nodes = [var_nodes]
              op_desc.set_input(input_name,
                                [var_node.name() for var_node in var_nodes])
          for output_name, var_nodes in six.iteritems(outputs):
              if not isinstance(var_nodes, list):
                  var_nodes = [var_nodes]
              op_desc.set_output(output_name,
                                 [var_node.name() for var_node in var_nodes])
          return IrOpNode(self.graph.create_op_node(op_desc))
  
      def create_op_node_from_desc(self, op_desc):
          """
          Create a operator node by using an existing OpDesc in the graph.
  
          Args:
              op_desc(core.VarDesc): the giving operator description.
  
          Returns:
              IrOpNode: the created operator node.
          """
          return IrOpNode(self.graph.create_op_node(op_desc))
  
      def update_input_link(self, old_input_node, new_input_node, op_node):
          """
          Update the input's link of a operator node.
  
          Args:
              old_input_node(IrNode): the old input node of the giving op_node.
              new_input_node(IrNode): the new input node of the giving op_node.
              op_node(IrOpNode): the operator node that is needed to update input's link.
          """
          assert old_input_node.node in self.graph.nodes() and new_input_node.node in \
              self.graph.nodes() and op_node.node in self.graph.nodes(), \
              'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
          old_input_node.remove_output(op_node)
          op_node.remove_input(old_input_node)
          new_input_node.append_output(op_node)
          op_node.append_input(new_input_node)
          op_node.rename_input(old_input_node.name(), new_input_node.name())
  
      def update_output_link(self, old_output_node, new_output_node, op_node):
          """
          Update the output's link of an operator node.
  
          Args:
              old_output_node(IrNode): the old output node of the giving op_node.
              new_output_node(IrNode): the new output node of the giving op_node.
              op_node(IrOpNode): the operator node that is needed to update input's link.
          """
          assert old_output_node.node in self.graph.nodes() and new_output_node.node in \
              self.graph.nodes() and op_node.node in self.graph.nodes(), \
              'The three arguments(old_output_node &new_output_node &op_node) must be in the graph nodes.'
          old_output_node.remove_input(op_node)
          op_node.remove_output(old_output_node)
          new_output_node.append_input(op_node)
          op_node.append_output(new_output_node)
          op_node.rename_output(old_output_node.name(), new_output_node.name())
  
      def link_to(self, node_in, node_out):
          """
          Connect two nodes.
  
          Args:
              node_in(IrNode): the input node.
              node_out(IrNode): the output node.
          """
          assert node_in.node in self.graph.nodes() and node_out.node in self.graph.nodes(), \
              'The two arguments(node_in&node_out) must be in the graph nodes.'
          node_in.append_output(node_out)
          node_out.append_input(node_in)
  
      def safe_remove_nodes(self, remove_nodes):
          """
          Remove nodes safely since links connected to these removed nodes are
          also removed.
  
          Args:
              remove_nodes(set): the nodes prepared to be removed.
          """
          if not isinstance(remove_nodes, set):
              if isinstance(remove_nodes, Iterable):
                  remove_nodes = set(remove_nodes)
              else:
                  remove_nodes = {remove_nodes}
          original_nodes = {n.node for n in remove_nodes}
          core.graph_safe_remove_nodes(self.graph, original_nodes)
  
      def resolve_hazard(self):
          ordered_nodes = core.topology_sort(self.graph)
          var_nodes = dict()
          for node in ordered_nodes:
              if node.is_op() and node.op() is not None:
                  for each_var_name in node.op().input_arg_names():
                      if each_var_name not in var_nodes:
                          var_nodes[each_var_name] = [
                              self._find_node_by_name(node.inputs, each_var_name)
                          ]
                  for each_var_name in node.op().output_arg_names():
                      if each_var_name not in var_nodes:
                          var_nodes[each_var_name] = [
                              self._find_node_by_name(node.outputs, each_var_name)
                          ]
                      else:
                          var_nodes[each_var_name].append(
                              self._find_node_by_name(node.outputs,
                                                      each_var_name))
          self.graph.resolve_hazard(var_nodes)
  
      def has_circle(self):
          """
          Check if the graph has a circle.
  
          Returns:
              bool: True if the graph has a circle else False.
          """
          return core.has_circle(self.graph)
  
      def graph_num(self):
          """
          Count the number of unconnected graphs in this graph.
  
          Returns:
              int: the number of unconnected graphs.
          """
          return core.graph_num(self.graph)
  
      def topology_sort(self):
          """
          Perform the topology sort operation on the graph.
  
          Notes: the `graph` can not contain a circle.
  
          Returns:
              list(IrNode): nodes in topology order.
          """
          ordered_nodes = core.topology_sort(self.graph)
          return [IrNode(n) for n in ordered_nodes]
  
      def build_adjacency_list(self):
          """
          Build an adjacency list of operations for the `graph`.
  
          Returns:
              dict{IrNode: set(IrNode)}: the adjacency list.
          """
          adj_list = core.build_adjacency_list(self.graph)
          wrapped_adj_list = dict()
          for k, v in six.iteritems(adj_list):
              wrapped_adj_list[IrNode(k)] = {IrNode(n) for n in v}
          return wrapped_adj_list
  
      def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
          """
          Draw the graph. If `dot` command is installed, the drawn graph
          will be saved as pdf file type, otherwise dot file type is used.
  
          Args:
              save_path(str): the save path of drawn graph.
              name(str): the name of drawn graph.
              marked_nodes(set(IrNode)): nodes that are needed to be marked.
              Default value is None.
              remove_ctr_var(bool): If it is set True, all control variable nodes
              in the graph will be removed. Default value is True.
          """
  
          def _convert_to_pdf(dot_file_path):
              pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
              exited_code = subprocess.call(
                  'dot -Tpdf ' + dot_file_path + ' -o ' + pdf_save_path,
                  shell=True)
              if exited_code != 0:
                  print('The dot command is needed for creating pdf files.')
                  print('The {} is saved as the dot filetype.'.format(
                      dot_file_path))
  
          remove_ctr_vars = set()
          if remove_ctr_var:
              for node in self.all_var_nodes():
                  if node.is_ctrl_var():
                      remove_ctr_vars.add(node)
              self.safe_remove_nodes(remove_ctr_vars)
          print('Total ops num = {}.'.format(len(self.all_op_nodes())))
  
          if marked_nodes is not None:
              if not isinstance(marked_nodes, set):
                  if isinstance(marked_nodes, Iterable):
                      marked_nodes = set(marked_nodes)
                  else:
                      marked_nodes = {marked_nodes}
              marked_nodes = {n.node for n in marked_nodes}
              remove_ctr_vars = {n.node for n in remove_ctr_vars}
              marked_nodes = marked_nodes - remove_ctr_vars
              if self.graph.has('__graphviz__marked_node__'):
                  self.graph.erase('__graphviz__marked_node__')
              self.graph.set('__graphviz__marked_node__', marked_nodes)
          if not os.path.exists(save_path):
              os.makedirs(save_path)
          viz_dot_path = os.path.join(save_path, name) + '.dot'
          viz_pass = core.get_pass('graph_viz_pass')
          viz_pass.set('graph_viz_path', viz_dot_path)
          viz_pass.apply(self.graph)
          _convert_to_pdf(viz_dot_path)
  
      def to_program(self):
          """
          Convert the graph into a Program.
  
          WARN: When the graph includes backward operator nodes, the
          conversion process may be failed. Usually, this function is
          only used to convert a test graph.
  
          Returns:
              Program: a program converted from the graph.
          """
          convert_pass = core.get_pass('graph_to_program_pass')
          desc = core.ProgramDesc()
          convert_pass.set_not_owned('program', desc)
          convert_pass.apply(self.graph)
          program = Program._construct_from_desc(desc)
          return program
  
      def _find_node_by_name(self, nodes, node_name):
          """
          Find a node in the giving nodes set by the name.
          """
          target_node = None
          for n in nodes:
              if n.name() == node_name:
                  target_node = n
          assert target_node is not None, "Cannot find the target node in the giving set."
          return target_node
  
      def _update_desc_attr(self, desc, name, val):
          """
          Update the value of desc's attribute by attribute's name.
          """
          if isinstance(val, Block):
              desc.set_block_attr(name, val.desc)
          elif isinstance(val, list) and val and all(
                  isinstance(v, Block) for v in val):
              desc.set_blocks_attr(name, [v.desc for v in val])
          elif isinstance(val, core.BlockDesc) or \
                  isinstance(val, core.ProgramDesc):
              desc.set_serialized_attr(name, val.serialize_to_string())
          else:
              desc._set_attr(name, val)
  
  
  class Program(object):
      """
      Create Python Program.  It has at least one :ref:`api_guide_Block_en`, when the
      control flow op like conditional_block, while :ref:`api_paddle_fluid_layers_While` is included,
      it will contain nested block.
  
      Please reference the
      `framework.proto <https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/framework.proto>`_
      for details.
  
      A set of Program usually contains startup program and main program.
      A startup program is set to contain some initial work, eg. initialize the ``Parameter``, and the main
      program will contain the network structure and vars for train.
  
      A set of Program can be used for test or train, in train program ,
      Paddle will contain all content to build a train network,  in test
      program Paddle will prune some content which is irrelevant to test, eg.
      backward ops and vars.
  
      **Notes**:
          **we have** :ref:`api_paddle_fluid_framework_default_startup_program` **and** :ref:`api_paddle_fluid_framework_default_main_program`
          **by default, a pair of them will shared the parameters. The** :ref:`api_paddle_fluid_framework_default_startup_program` **only run once to initialize parameters,**
          :ref:`api_paddle_fluid_framework_default_main_program` **run in every mini batch and adjust the weights.**
  
      Returns:
          Program: An empty Program.
  
      Examples:
          .. code-block:: python
  
              import paddle
              import paddle.static as static
  
              paddle.enable_static()
  
              main_program = static.Program()
              startup_program = static.Program()
              with static.program_guard(main_program=main_program, startup_program=startup_program):
                  x = static.data(name="x", shape=[-1, 784], dtype='float32')
                  y = static.data(name="y", shape=[-1, 1], dtype='int32')
                  z = static.nn.fc(name="fc", x=x, size=10, activation="relu")
  
              print("main program is: {}".format(main_program))
              print("start up program is: {}".format(startup_program))
  
      """
  
      def __init__(self):
          self.desc = core.ProgramDesc()
          self.blocks = [Block(self, 0)]
          self.current_block_idx = 0
          global global_prog_seed
          self._seed = global_prog_seed
          self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
          self.__op_role_var = []
  
          # for distribute training
          # _is_distributed = True if under distributed training
          self._is_distributed = False
          # _is_chief = True if the trainer is the first one, usually No.0
          self._is_chief = False
          # _parameters_on_pservers records all the parameters distributed on parameter servers.
          self._parameters_on_pservers = None
          # _endpoints is a list about parameter servers ip:port, such as ["ip:port","ip:port"]
          self._endpoints = []
          # if current role is parameter server, the _ps_endpoint is its "ip:port"
          self._ps_endpoint = None
          # trainers_endpoints, it is used for distribution.
          self._trainers_endpoints = []
          # the distributed lookup table names
          self._distributed_lookup_table = None
  
          # use Deep gradient comrepssion or not
          self._enable_dgc = False
          self._use_lamb = False
  
          self._nccl_comm_num = 1
          self._use_hierarchical_allreduce = False
          self._hierarchical_allreduce_inter_nranks = 0
  
          # if this program has been optimized by distributed optimizer
          # fleet_opt will be given a value
          self._fleet_opt = None
          self._program_config = None
  
          # assigned if this program has been parsed by a pipeline optimizer
          self._pipeline_opt = None
  
          # appending gradients times
          self._appending_grad_times = 0
  
          # identifier for auto checkpoint
          self._auto_checkpoint_name = unique_name.generate(
              "__auto_checkpoint_program__")
  
          # compiled program, i.e. Graph
          self._graph = None
  
      def global_seed(self, seed=0):
          """
          Set global seed for Program
  
          Returns:
              None.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  print(prog.random_seed)
                  ## 0
                  ## the default random seed is 0
  
                  prog.global_seed(102)
                  prog1 = static.default_main_program()
                  print(prog1.random_seed)
                  ## 102
                  ## the random seed is 102
          """
          global global_prog_seed
          global_prog_seed = seed
          self._seed = global_prog_seed
  
      @property
      def _op_role(self):
          """
          The operator role. In a enum {Forward, Backward, Optimize}.
  
          Notes: this is a low level API. It is used only for ParallelExecutor to
          duplicate or schedule operator to devices.
  
          For example, the forward operator should be executed on every device.
          The backward operator should be executed on every device and the
          parameter gradient of backward (use :code:`_op_role_var` to get this
          variable) operator should be merged to one device. The optimization
          operators should be executed on only one device and broadcast the
          optimization result, i.e., the new parameter, to every other device.
          """
          return self._current_role
  
      @_op_role.setter
      def _op_role(self, role):
          self._current_role = role
  
      @property
      def _op_role_var(self):
          """
          The auxiliary variables for :code:`_op_role` property.
  
          See Also: :code:`Program._op_role`'s documentation for details.
  
          Notes: This is a very low-level API. Users should not use it directly.
          """
          return self.__op_role_var
  
      @signature_safe_contextmanager
      def _backward_role_guard(self):
          tmp_role = self._current_role
  
          OpRole = core.op_proto_and_checker_maker.OpRole
          self._current_role = OpRole.Backward
          try:
              yield
          finally:
              self._current_role = tmp_role
  
      @signature_safe_contextmanager
      def _optimized_guard(self, param_and_grads):
          """
          A with guard to set :code:`Optimization` :code:`OpRole` and
          :code:`OpRoleVar` automatically.
  
          Notes: This is a very low level API. Users should not use it directly.
  
          Args:
              param_and_grads(list): The variables (names) to be optimized.
  
          Examples:
  
              >>> import paddle.fluid as fluid
              >>> p, g = backward(...)
              >>> with program._optimized_guard([p,g]):
              >>>     p = p - 0.001 * g
          """
          tmp_role = self._current_role
          tmp_var = self.__op_role_var
  
          OpRole = core.op_proto_and_checker_maker.OpRole
          self._current_role = OpRole.Optimize
          self.__op_role_var = [
              var.name if isinstance(var, Variable) else var
              for var in param_and_grads
          ]
          try:
              yield
          finally:
              self.__op_role_var = tmp_var
              self._current_role = tmp_role
  
      @signature_safe_contextmanager
      def _lr_schedule_guard(self, is_with_opt=False):
          """
          A with guard to set :code:`LRSched` :code:`OpRole` and
          :code:`OpRoleVar` automatically. The :code:`OpRoleVar` is
          set to the target learning rate.
  
          Notes: This is a very low level API. Users should not use it directly.
  
          Args:
              is_with_opt: Only set to true if these ops a in the middle
                   of a bunch of optimize ops so that it can be treated
                   correctly. For example, sgd->lr_op->sgd->lr_op->sgd.
  
          Examples:
  
              >>> import paddle.fluid as fluid
              >>> p, g = backward(...)
              >>> with program.lr_schedule_guard():
              >>>     lr = lr * decay
          """
  
          tmp_role = self._current_role
          tmp_var = self.__op_role_var
  
          OpRole = core.op_proto_and_checker_maker.OpRole
          self._current_role = OpRole.LRSched
          if is_with_opt:
              self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
          # TODO(typhoonzero): how to set target learning rate var
          self.__op_role_var = []
          try:
              yield
          finally:
              self.__op_role_var = tmp_var
              self._current_role = tmp_role
  
      def __str__(self):
          """
          Get the protobuf debug string of this Program.
  
          Returns:
              (str): The protobuf debug string.
  
          Raises:
              ValueError: If any of required fields is not set.
          """
          return self._to_readable_code()
  
      def _to_readable_code(self, skip_op_callstack=True):
          """
          Get readable debug string of Program.
  
          .. note::
              If you want to get the debug string in protobuf format,
              please use :code:`to_string` method.
  
          Args:
              skip_op_callstack(bool): whether to skip parsing Operator's attribute
                  op_callstack, default value is True
  
          Returns:
              string: The formatted Program string.
  
          Examples:
              .. code-block:: python
  
              import paddle
              import paddle.static as static
  
              paddle.enable_static()
  
              cur_program = static.Program()
              cur_block = cur_program.current_block()
              new_var = cur_block.create_var(name="X",
                                             shape=[-1, 23, 48],
                                             dtype='float32')
              new_op = cur_block.append_op(type="abs",
                                  inputs={"X": [new_var]},
                                  outputs={"Out": [new_var]})
              print(cur_program._to_readable_code())
          """
          assert isinstance(
              skip_op_callstack, bool
          ), "skip_op_callstack parameter's type is error, expect bool, received %s".format(
              type(skip_op_callstack))
          program_str = ""
          for block in self.blocks:
              program_str += block._to_readable_code(skip_op_callstack)
              program_str += '\n'
          return program_str
  
      def to_string(self, throw_on_error, with_details=False):
          """
          To debug string.
  
          Args:
  
              throw_on_error (bool): raise Value error when any of required fields is not set.
  
              with_details (bool): True if more details about variables and parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need to print.
  
          Returns:
              str: The debug string describe current Program.
  
          Raises:
              ValueError: If any of required fields is not set and throw_on_error is True.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  x = static.data(name="X", shape=[2,3], dtype="float32")
                  pred = static.nn.fc(x, size=3)
                  prog_string = prog.to_string(throw_on_error=True, with_details=False)
                  prog_string_with_details = prog.to_string(throw_on_error=False, with_details=True)
                  print("program string without detail: {}".format(prog_string))
                  print("program string with detail: {}".format(prog_string_with_details))
          """
          assert isinstance(
              throw_on_error, bool
          ), "The type of throw_on_error parameter is wrong, expected bool, but received {}.".format(
              type(throw_on_error))
          assert isinstance(
              with_details, bool
          ), "The type of with_details parameter is wrong, expected bool, but received {}.".format(
              type(with_details))
  
          if with_details:
              res_str = ""
              for block in self.blocks:
                  res_str += block.to_string(throw_on_error, with_details)
          else:
              protostr = self.desc.serialize_to_string()
              proto = framework_pb2.ProgramDesc.FromString(
                  six.binary_type(protostr))
              res_str = _debug_string_(proto, throw_on_error)
          return res_str
  
      def _get_desc(self):
          """
          Get the C++ side of `ProgramDesc` object pointer. The C++ object is
          exposed by :code:`pybind`.
  
          Notes: This is a very low level API. Users should not use this API
          directly.
          """
          return self.desc
  
      def _version(self):
          return self.desc._version()
  
      def clone(self, for_test=False):
          """
          .. note:::
              1. :code:`Program.clone()` method DOES NOT clone :ref:`api_paddle_io_DataLoader` . 
              2. Recommend you to use :code:`clone` before using :code:`Opimizer.minimize` . 
              3. This API has no effect in Dygraph Mode.
  
          Create a new Program with forward content of original one when ``for_test=True``.
          Create a new Program as same as the original one when ``for_test=False``.
  
          Some operators, e.g., :ref:`api_paddle_fluid_layers_batch_norm` , behave differently between
          training and testing. They have an attribute, :code:`is_test`, to
          control this behaviour. This method will change the :code:`is_test`
          attribute of them to :code:`True` when :code:`for_test=True`.
  
          * Set for_test to False when you want to clone the program for training.
          * Set for_test to True when you want to clone the program for testing.
            We will prune the backward and optimize part of the program when you
            use :code:`clone` after :code:`Opimizer.minimize`, but we still
            recommend you to use :code:`clone` before using :code:`Opimizer.minimize`.
  
          For Example:
            ::
  
              import paddle
              import paddle.static as static
  
              paddle.enable_static()
  
              img = static.data(name='image', shape=[None, 784])
              pred = static.nn.fc(x=img, size=10, actvation='relu')
              loss = paddle.mean(pred)
              # Here we use clone before Momentum
              test_program = static.default_main_program().clone(for_test=True)
              optimizer = paddle.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
              optimizer.minimize(loss)
  
          Args:
  
              for_test (bool): True if change the :code:`is_test` attribute of operators to :code:`True`
                  and prune the backward and optimize part of the program. The default value is :code:`False` .
  
          Returns:
              Program: A new Program with forward content of original one when ``for_test=True``.  A new Program as same as the original one when ``for_test=False``
  
  
          Examples:
  
              .. note::
                  The Program's order maybe different after :code:`clone` and
                  this will not affect your training or testing progress. In the following
                  example we give you an simple method :code:`print_prog(program)` to
                  print Program Descs inorder to make sure you have same print result
                  after :code:`clone`:
  
              .. code-block:: python
  
                  import six
  
                  def print_prog(prog):
                      for name, value in sorted(six.iteritems(prog.block(0).vars)):
                          print(value)
                      for op in prog.block(0).ops:
                          print("op type is {}".format(op.type))
                          print("op inputs are {}".format(op.input_arg_names))
                          print("op outputs are {}".format(op.output_arg_names))
                          for key, value in sorted(six.iteritems(op.all_attrs())):
                              if key not in ['op_callstack', 'op_role_var']:
                                  print(" [ attrs: {}:   {} ]".format(key, value))
  
  
              1. To clone a test program, the sample code is:
                  .. code-block:: python
  
                      import six
                      import paddle
                      import paddle.static as static
                      import paddle.utils as utils
                      import paddle.nn.functional as F
  
                      paddle.enable_static()
  
                      def print_prog(prog):
                          for name, value in sorted(six.iteritems(prog.block(0).vars)):
                              print(value)
                          for op in prog.block(0).ops:
                              print("op type is {}".format(op.type))
                              print("op inputs are {}".format(op.input_arg_names))
                              print("op outputs are {}".format(op.output_arg_names))
                              for key, value in sorted(six.iteritems(op.all_attrs())):
                                  if key not in ['op_callstack', 'op_role_var']:
                                      print(" [ attrs: {}:   {} ]".format(key, value))
  
                      train_program = static.Program()
                      startup_program = static.Program()
  
                      # startup_program is used to do some parameter init work,
                      # and main program is used to hold the network
                      with static.program_guard(train_program, startup_program):
                          with utils.unique_name.guard():
                              img = static.data(name='image', shape=[None, 784])
                              hidden = static.nn.fc(x=img, size=200, activation='relu')
                              hidden = F.dropout(hidden, p=0.5)
                              loss = F.cross_entropy(
                                  input=static.nn.fc(x=hidden, size=10, activation='softmax'),
                                  label=static.data(name='label', shape=[1], dtype='int64'))
                              avg_loss = paddle.mean(loss)
                              test_program = train_program.clone(for_test=True)
                      print_prog(test_program)
  
                      # Due to parameter sharing usage for train and test, so we need to use startup program of train
                      # instead of using test startup program, while nothing is in test's startup program
  
                      # In Paddle we will share weights by using the same Tensor name. In train and test program
                      # all parameters will have the same name and this can make train and test program sharing parameters,
                      # that's why we need to use startup program of train. And for startup program of test, it has nothing,
                      # since it is a new program.
  
                      with static.program_guard(train_program, startup_program):
                          with utils.unique_name.guard():
                              sgd = paddle.optimizer.SGD(learning_rate=1e-3)
                              sgd.minimize(avg_loss)
  
  
              2. The clone method can be avoid if you create program for training and program for testing individually.
                  .. code-block:: python
  
                      import six
                      import paddle
                      import paddle.static as static
                      import paddle.utils as utils
                      import paddle.nn.functional as F
  
                      paddle.enable_static()
  
                      def print_prog(prog):
                          for name, value in sorted(six.iteritems(prog.block(0).vars)):
                              print(value)
                          for op in prog.block(0).ops:
                              print("op type is {}".format(op.type))
                              print("op inputs are {}".format(op.input_arg_names))
                              print("op outputs are {}".format(op.output_arg_names))
                              for key, value in sorted(six.iteritems(op.all_attrs())):
                                  if key not in ['op_callstack', 'op_role_var']:
                                      print(" [ attrs: {}:   {} ]".format(key, value))
  
                      def network():
                          img = static.data(name='image', shape=[None, 784])
                          hidden = static.nn.fc(x=img, size=200, activation='relu')
                          hidden = F.dropout(hidden, p=0.5)
                          loss = F.cross_entropy(
                              input=static.nn.fc(x=hidden, size=10, activation='softmax'),
                              label=static.data(name='label', shape=[1], dtype='int64'))
                          avg_loss = paddle.mean(loss)
                          return avg_loss
  
                      train_program_2 = static.Program()
                      startup_program_2 = static.Program()
                      test_program_2 = static.Program()
                      with static.program_guard(train_program_2, startup_program_2):
                          with utils.unique_name.guard():
                              avg_loss = network()
                              sgd = paddle.optimizer.SGD(learning_rate=1e-3)
                              sgd.minimize(avg_loss)
                      # the test startup program is not used.
                      with static.program_guard(test_program_2, startup_program_2):
                          with utils.unique_name.guard():
                              avg_loss = network()
                      print_prog(test_program_2)
  
              The two code snippets above will generate and print same programs.
          """
  
          # NOTE(zhiqiu): we sync the original program first, since its program may diff with
          # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc.
          self._sync_with_cpp()
  
          pruned_origin_block_id_map = None
          if for_test:
              forward_prog = Program()
              forward_prog.desc, pruned_origin_block_id_map = core.prune_backward(
                  self.desc)
              forward_prog.blocks = [
                  Block(forward_prog, i)
                  for i in six.moves.range(forward_prog.desc.num_blocks())
              ]
              forward_prog._sync_with_cpp()
              p = forward_prog._inference_optimize(prune_read_op=False)
          else:
              p = Program()
              p.current_block_idx = self.current_block_idx
              p._seed = self._seed
              p.desc = core.ProgramDesc(self.desc)
              p.blocks = [
                  Block(p, i) for i in six.moves.range(self.desc.num_blocks())
              ]
  
              p._current_role = self._current_role
              p.__op_role_var = self.__op_role_var
              p._appending_grad_times = self._appending_grad_times
              if hasattr(self, 'lr_sheduler'):
                  p.lr_sheduler = self.lr_sheduler
  
              # NOTE(zhiqiu): we sync the cloned program, to update its program by
              # its desc.
              p._sync_with_cpp()
  
          p._copy_param_info_from(self)
          p._copy_data_info_from(self, pruned_origin_block_id_map)
          p._copy_dist_param_info_from(self)
          return p
  
      def _prune(self, targets):
          """
          Prune operators and variables which are not needed to generate
          :code:`targets`.
  
          Notes: This is a very low level API. Users should not use this API
          directly. This API is in flux and not stable.
  
          Args:
              targets(list|Variable|Operator): A list of variables, operators, or variable names
                  need to be pruned
  
          Returns:
              Program:  A new, pruned program.
          """
          return self._prune_with_input([], targets)
  
      def _prune_with_input(self, feeded_var_names, targets):
          """
          Prune operators and variables which are not needed to generate
          :code:`targets`. Prune operators and variables which are needed 
          to generate feeded_var 
  
          Notes: This is a very low level API. Users should not use this API
          directly. This API is in flux and not stable.
  
          Args:
              feeded_var_names(list|str): A list of variable names from where
                  pruning start. If it is set as [], this API works just like _prune()
              targets(list|Variable|Operator): A list of variables, operators, or variable names
                  need to be pruned
  
          Returns:
              Program:  A new, pruned program.
          """
  
          # NOTE(zhiqiu): we sync the original program first, since its program may diff with
          # its desc due to modifying desc in c++ space. E.g. save op will add kLookupTablePath in desc.
          self._sync_with_cpp()
  
          if not isinstance(feeded_var_names, list):
              feeded_var_names = [feeded_var_names]
          if not isinstance(targets, list):
              targets = [targets]
  
          for var in feeded_var_names:
              if not isinstance(var, six.string_types):
                  raise ValueError(
                      "All feeded_var_names of Program._prune_with_input() can only be "
                      "str, but received %s." % type(var))
  
          targets_idx = []
          for t in targets:
              if not isinstance(t, Operator):
                  if isinstance(t, Variable):
                      name = t.name
                  elif isinstance(t, six.string_types):
                      name = str(t)
                  else:
                      raise ValueError(
                          "All targets of Program._prune_with_input() can only be "
                          "Variable or Operator, but received %s." % type(t))
  
                  # NOTEZ(zhiqiu): For variable to be fed in fetch_list, there two cases:
                  # (1) the variable is leaf, it has no op that generates it;
                  # (2) the variable is not leaf, and we need to prune the op that generates it.
                  # In both cases, wo can just skip target_op of that it.
                  if name in feeded_var_names:
                      continue
  
                  # After transpiler processing, the op that output this
                  # variable maybe has been changed, so t.op is not reliable
                  # and we need to find the current op that generate this
                  # variable here.
                  target_op = None
                  global_block = self.global_block()
                  for idx, op in enumerate(global_block.ops):
                      if name in op.output_arg_names:
                          # NOTE(zhiqiu): Find op that generate target name.
                          # Skip optimize op except for optimize op in targets,
                          # since optimize op generates parameters.
                          if op._is_optimize_op() and op not in targets:
                              continue
                          else:
                              target_op = op
                              break
                  if target_op is None:
                      raise ValueError(
                          "The target variable used for pruning should have an "
                          "associated operator that generates it.")
                  else:
                      targets_idx.append([target_op.block.idx, target_op.idx])
              else:
                  targets_idx.append([t.block.idx, t.idx])
  
          res = Program()
          res.desc, pruned_origin_block_id_map = core.prune(self.desc,
                                                            set(feeded_var_names),
                                                            targets_idx)
          res.blocks = [
              Block(res, i) for i in six.moves.range(res.desc.num_blocks())
          ]
          res._sync_with_cpp()
  
          res._copy_param_info_from(self)
          res._copy_data_info_from(self, pruned_origin_block_id_map)
          res._copy_dist_param_info_from(self)
  
          return res
  
      def _inference_optimize(self, prune_read_op=True):
          """
          This method will create a new program and do following adjustments on it:
          1. Remove all reader variables and their creator ops if exist.
  
          2. Remove the :code:`read_op` if exists.
  
          3. change the :code:`is_test`
          attribute of operators to :code:`True`. All the :code:`Parameter`
          information will be lost.
  
          Args:
              prune_read_op(bool): remove the read ops that are added by py_reader
                                   for cpp inference library
  
          Notes: This API is a very low level API. Use
          :code:`Program.clone(for_test=True)` instead.
  
          Returns:
              Program: The new program.
          """
          res = Program()
          res.desc = core.ProgramDesc(self.desc)
  
          # remove all readers and the read_op if exist
          read_op_idx = 0
          root_block = res.desc.block(0)
          if prune_read_op:
              while True:
                  if read_op_idx >= root_block.op_size() or root_block.op(
                          read_op_idx).type() == 'read':
                      break
                  read_op_idx += 1
              if read_op_idx < root_block.op_size():
                  root_block._remove_op(0, read_op_idx + 1)
              for var in root_block.all_vars():
                  if var.type() == core.VarDesc.VarType.READER:
                      root_block._remove_var(cpt.to_bytes(var.name()))
  
          # change all `is_test` attributes to True
          for i in six.moves.range(res.desc.num_blocks()):
              block = res.desc.block(i)
              for j in six.moves.range(block.op_size()):
                  op = block.op(j)
                  if op.has_attr('is_test'):
                      op._set_attr('is_test', True)
                  if op.type() == "batch_norm":
                      # Remove the output ReserveSpace of batch_norm if exists.
                      op.remove_output("ReserveSpace")
          res.blocks = [
              Block(res, i) for i in six.moves.range(res.desc.num_blocks())
          ]
          res._sync_with_cpp()
          return res
  
      @staticmethod
      def parse_from_string(binary_str):
          """
          .. note::
              1. All information about parameters will be lost after serialization; 
              2. This API has no effect in Dygraph mode.
  
          Deserialize a Program from  `protobuf <https://en.wikipedia.org/wiki/Protocol_Buffers>`_  binary string.
          This method always use to save and load model
  
          Args:
  
              binary_str_type (str): the binary prootbuf string.
  
          Returns:
              Program: A deserialized Program.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  startup_prog = static.Program()
                  main_prog = static.Program()
                  with static.program_guard(startup_prog, main_prog):
                      x = static.data(name='X', shape=[1000, 784], dtype='float32')
  
                      y = static.data(name='Y', shape=[784, 100], dtype='float32')
  
                      z = paddle.matmul(x=x, y=y)
  
                      binary_str = static.default_main_program().desc.serialize_to_string()
                      prog_restored = static.default_main_program().parse_from_string(binary_str)
  
                      print(static.default_main_program())
                      print(prog_restored)
          """
          p = Program()
          p.desc = core.ProgramDesc(binary_str)
          p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
          p._sync_with_cpp()
          return p
  
      @staticmethod
      def _construct_from_desc(desc):
          """
          Construct a program from program desc.
  
          Args:
              desc(core.ProgramDesc): The program desc for constructing.
  
          Returns:
              Program: A program.
          """
          p = Program()
          p.desc = desc
          p.blocks = [Block(p, i) for i in six.moves.range(p.desc.num_blocks())]
          p._sync_with_cpp()
          return p
  
      @property
      def random_seed(self):
          """
          The default random seed for random operators in Program. ``0`` means get
          the random seed from random device.
  
          .. note:: 
              It must be set before the operators have been added.
  
          Returns:
              int64: Random seed in current Program
  
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
                  import paddle.nn.functional as F
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  random_seed = prog.random_seed
                  x_var = static.data(name="X", shape=[3,3], dtype="float32")
                  print(random_seed)
                  ## 0
                  ## the default random seed is 0
  
                  # Here we need to set random seed before we use paddle.nn.functional.dropout
                  prog.random_seed = 1
                  z_var = F.dropout(x_var, 0.7)
  
                  print(prog.random_seed)
                  ## 1
                  ## the random seed is change to 1
          """
          return self._seed
  
      @property
      def num_blocks(self):
          """
          The number of :ref:`api_guide_Block_en`  in this Program.
  
          .. note:: 
              This API has no effect in Dygraph mode.
  
          Returns:
              int(Platform-dependent size): num of :ref:`api_guide_Block_en`  in current Program
  
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  num_blocks = prog.num_blocks
                  print(num_blocks)
  
                  # print result:
                  # 1
          """
          return self.desc.num_blocks()
  
      @random_seed.setter
      def random_seed(self, seed):
          if not isinstance(seed, int):
              raise ValueError(
                  "Program.random_seed's input seed must be an integer, but received %s."
                  % type(seed))
          self._seed = seed
  
      def __repr__(self):
          return self.__str__()
  
      def global_block(self):
          """
          .. note::
              This API has no effect in Dygraph mode.
  
          Get the first :ref:`api_guide_Block_en` of this Program.
  
          Returns:
              :ref:`api_guide_Block_en`: The first :ref:`api_guide_Block_en`  of this Program.
  
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  gb_block = prog.global_block()
                  print(gb_block)
  
          """
          return self.blocks[0]
  
      def block(self, index):
          """
          .. note::
              This API has no effect in Dygraph mode.
  
          Get the :code:`index`  :ref:`api_guide_Block_en`  of this Program
  
          Args:
              index (int) - The index of  :ref:`api_guide_Block_en`  to get
  
          Returns:
              :ref:`api_guide_Block_en`: The :code:`index` block
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  block_0 = prog.block(0)
                  print(block_0)
          """
          return self.blocks[index]
  
      def current_block(self):
          """
          .. note::
              This API has no effect in Dygraph mode.
  
          Get the current  :ref:`api_guide_Block_en` . The :code:`current`  :ref:`api_guide_Block_en`
          is the  :ref:`api_guide_Block_en`  to append operators.
  
          Returns:
               :ref:`api_guide_Block_en`: The :code:`index`  :ref:`api_guide_Block_en`
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  current_blk = prog.current_block()
                  print(current_blk)
          """
          return self.blocks[self.current_block_idx]
  
      def _create_block(self, parent_idx=None):
          """
          Create a new block with the :code:`parent_idx` and change the current block
          to new block.
  
          Args:
  
              parent_idx(int): The parent block index.
  
          Returns:
              Block: The new block.
          """
          new_block_idx = len(self.blocks)
          parent = self.current_block() if parent_idx is None else self.block(
              parent_idx)
          self.desc.append_block(parent.desc)
          self.current_block_idx = new_block_idx
          self.blocks.append(Block(self, self.current_block_idx))
          return self.current_block()
  
      def _rollback(self):
          """
          Exit a code block, i.e., roll back to the parent block.
          Returns:
              None
          """
          self.current_block_idx = self.current_block().parent_idx
  
      def _sync_with_cpp(self):
          """
          Synchronize Python instance to its binding C++ object instance.
          If the program is modified in C++ space, this method should be invoked.
  
          Notes: This is a very low level API. Users should not invoke it
          directly.
  
          Returns:
              None
          """
          for block_idx in range(len(self.blocks), self.desc.num_blocks()):
              self.blocks.append(Block(self, block_idx))
          for block in self.blocks:
              block._sync_with_cpp()
  
      def _copy_param_info_from(self, other):
          """
          Copy the information of parameters from other program.
  
          Notes: This is a very low level API. Users should not invoke it
          directly.
  
          Args:
              other(Program): Other program
  
          Returns:
              None
          """
          if not isinstance(other, Program):
              raise TypeError(
                  "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                  % type(other))
  
          self.global_block()._copy_param_info_from(other.global_block())
  
      def _copy_dist_param_info_from(self, other):
          """
          Copy the information of distributed information from other program.
  
          Args:
              other(Program): Other program
  
          Returns:
              None
          """
          if not isinstance(other, Program):
              raise TypeError(
                  "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                  % type(other))
          self._is_distributed = other._is_distributed
          self._is_chief = other._is_chief
          self._parameters_on_pservers = other._parameters_on_pservers
          self._endpoints = other._endpoints
          self._ps_endpoint = other._ps_endpoint
          self._distributed_lookup_table = other._distributed_lookup_table
  
      def _copy_data_info_from(self, other, pruned_origin_block_id_map=None):
          """
          Copy the information of data variables from other program.
  
          Notes: This is a very low level API. Users should not invoke it
          directly.
  
          Args:
              other(Program): Other program
              pruned_origin_block_id_map(dict{int:int}): A dict which maps the block id in program
              self to the block id in program other. For example, {0:0, 1:1, 2:3} means block 0 in self is 
              cloned from block 0 in other, etc. Default is None, which means default mapped, 
              {0:0, 1:1,..., n:n}.
  
          Returns:
              None
          """
          if not isinstance(other, Program):
              raise TypeError(
                  "Function Program._copy_param_info_from() needs to pass in a source Program, but received %s"
                  % type(other))
  
          if not pruned_origin_block_id_map:
              pruned_origin_block_id_map = {
                  i: i
                  for i in six.moves.range(self.desc.num_blocks())
              }
  
          # NOTE(zhiqiu): All vars in cloned program exist in original program.
          # The reverse is not true, due to backward pruning.
          for i, block in enumerate(self.blocks):
              other_block = other.blocks[pruned_origin_block_id_map[i]]
              for var in list(block.vars.values()):
                  other_var = other_block.var(var.name)
                  if other_var.is_data:
                      var.is_data = True
                  if other_var.desc.need_check_feed():
                      var.desc.set_need_check_feed(True)
                  if other_var.stop_gradient:
                      var.stop_gradient = True
  
      def list_vars(self):
          """
          Get all Tensors from this Program. A iterable object is returned.
  
          Returns:
              iterable Tensors: The Generator will yield every Tensor in this program.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  prog = static.default_main_program()
                  img = static.data(name='img', shape=[None, 1,28,28], dtype='float32')
                  label = static.data(name='label', shape=[None,1], dtype='int64')
                  for var in prog.list_vars():
                      print(var)
  
                  # var img : paddle.VarType.LOD_TENSOR.shape(-1, 1, 28, 28).astype(VarType.FP32)
                  # var label : paddle.VarType.LOD_TENSOR.shape(-1, 1).astype(VarType.INT64)
          """
          for each_block in self.blocks:
              for each_var in list(each_block.vars.values()):
                  yield each_var
  
      def all_parameters(self):
          """
          Get all :ref:`api_guide_parameter_en` from this Program. A list object is returned.
  
          Returns:
              list[ :ref:`api_guide_parameter_en` ]: The list contians all parameters in this program.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  program = static.default_main_program()
                  data = static.data(name='x', shape=[None, 13], dtype='float32')
                  hidden = static.nn.fc(x=data, size=10)
                  loss = paddle.mean(hidden)
                  paddle.optimizer.SGD(learning_rate=0.01).minimize(loss)
  
                  for param in program.all_parameters():
                      print(param)
  
                  # Here will print all parameters in current program, in this example,
                  # the result is like:
                  #
                  # persist trainable param fc_0.w_0 : paddle.VarType.LOD_TENSOR.shape(13, 10).astype(VarType.FP32)
                  # persist trainable param fc_0.b_0 : paddle.VarType.LOD_TENSOR.shape(10,).astype(VarType.FP32)
                  #
                  # Here print(param) will print out all the properties of a parameter,
                  # including name, type and persistable, you can access to specific
                  # property of a parameter, such as param.name, param.type
          """
          parameters = []
          for each_block in self.blocks:
              parameters.extend(each_block.all_parameters())
          return parameters
  
      def state_dict(self, mode='all', scope=None):
          """
          Get parameters and persistable buffers of program as a dict. The key is the name of the parameter or the name of the buffer.
          The value is the tensor of this variable in the given scope.
  
          .. note::
              This function MUST called after run start_up_program
  
          Args:
              mode(str, optional): Source of the obtained parameters and buffers. 
                      'opt' :  The return value only contains the variable in the optimizer. 
                      'param' : The return value only contains the variable in the network, not the variable in the optimizer.  
                      'all' : The return value contains the variable in the network and optimizer.
                      Default: 'all'
              scope(Scope, optional) : If scope is None, state_dict will be set to global scope 
                  obtained through 'paddle.static.global_scope()'. Otherwise, value will be set to scope.
                  Default: None
  
          Retruns:
              dict: a dict contains the parameters and persistable buffers.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  x = static.data(name="x", shape=[10, 10], dtype='float32')
                  y = static.nn.fc(x, 10)
                  z = static.nn.fc(y, 10)
  
                  place = paddle.CPUPlace()
                  exe = static.Executor(place)
                  exe.run(static.default_startup_program())
                  prog = static.default_main_program()
  
                  path = "./temp/model.pdparams"
                  paddle.save(prog.state_dict(), path)
          """
          # The 'framework' is a low-level module, and 'executor'
          # can not be imported at the begainning of this file. 
          # Therefore, the above two modules are dynamically imported.
          from .executor import global_scope
          if scope is not None and not isinstance(scope, core._Scope):
              raise TypeError(
                  "`scope` should be None or `paddle.static.Scope'` type, but received {}.".
                  format(type(scope)))
  
          if scope is None:
              scope = global_scope()
  
          if not isinstance(mode, str):
              raise TypeError("Type of `mode` should be string, but received {}.".
                              format(type(mode)))
  
          def is_parameter(var):
              return isinstance(var, Parameter)
  
          def is_persistable(var):
              if var.desc.type() == core.VarDesc.VarType.FEED_MINIBATCH or \
                  var.desc.type() == core.VarDesc.VarType.FETCH_LIST or \
                  var.desc.type() == core.VarDesc.VarType.READER:
                  return False
              return var.persistable
  
          def is_belong_to_optimizer(var):
              if not (isinstance(var, Parameter) or var.desc.need_check_feed()):
                  return is_persistable(var)
              return False
  
          def condition(var):
  
              if mode == 'param':
                  return is_parameter(var)
              elif mode == 'opt':
                  return is_belong_to_optimizer(var)
              elif mode == 'all':
                  return is_parameter(var) or is_belong_to_optimizer(var)
              else:
                  raise ValueError(
                      "`mode` string should be 'param', 'opt' or 'all', but received {}.".
                      format(mode))
  
          var_list = filter(condition, self.list_vars())
  
          state_dict = dict()
          for var in var_list:
              var_temp = scope.find_var(var.name)
              if var_temp is None:
                  raise ValueError(
                      "Can not find Variable '{}' in the scope. Make sure it is initialized".
                      format(var.name))
              state_dict[var.name] = var_temp.get_tensor()
  
          return state_dict
  
      def set_state_dict(self, state_dict, scope=None):
          """
          Set parameters and persistable buffers in state_dict to program. 
          An exception will throw if shape or dtype of the parameters is not match.
          
          .. note::
              This function MUST called after run start_up_program
  
          Args:
              state_dict(dict): the dict store parameters and persistable buffers. 
                  The key is the name of the parameter or the name of the buffer.
                  The value is the tensor of this variable in the given scope.
              scope(Scope, optional) : If scope is None, state_dict will be set to global scope 
                  obtained through 'paddle.static.global_scope()'. Otherwise, value will be set to scope.
                  Default: None
          
          Returns:
              None
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import paddle.static as static
  
                  paddle.enable_static()
  
                  x = static.data(name="x", shape=[10, 10], dtype='float32')
                  y = static.nn.fc(x, 10)
                  z = static.nn.fc(y, 10)
  
                  place = paddle.CPUPlace()
                  exe = static.Executor(place)
                  exe.run(static.default_startup_program())
                  prog = static.default_main_program()
  
                  path = "./temp/model.pdparams"
                  paddle.save(prog.state_dict(), path)
                  state_dict_load = paddle.load(path)
                  prog.set_state_dict(state_dict_load)
          """
  
          if not isinstance(state_dict, dict):
              raise TypeError(
                  "Type of `state_dict` should be dict, but received {}.".format(
                      type(state_dict)))
  
          vars_dict = {var.name: var for var in self.list_vars()}
          condition = True if 'StructuredToParameterName@@' in state_dict else False
          for name, value in state_dict.items():
              if condition:
                  if name == "StructuredToParameterName@@":
                      continue
                  if name in state_dict['StructuredToParameterName@@']:
                      name = state_dict['StructuredToParameterName@@'][name]
              if name in vars_dict:
                  try:
                      vars_dict[name].set_value(value, scope)
                  except ValueError as err:
                      warnings.warn(
                          ("Skip loading for '{}'. ".format(name) + str(err)))
                  except TypeError as err:
                      warnings.warn(
                          ("Skip loading for '{}'. ".format(name) + str(err)))
              else:
                  warnings.warn((
                      "Skip loading for '{0}'. Because '{0}' not in the program.".
                      format(name)))
  
  
  @six.add_metaclass(ParameterMetaClass)
  class Parameter(Variable):
      """
      Parameter is derived from Variable. A parameter is a persistable
      Variable, and will be updated by optimizers after each iteration.
      The training of a neural network is essentially the updating of
      its parameters.
  
      Relative to a general Variable, a Parameter has several its own
      member variables:
  
      Args:
          trainable(bool): True if the parameter need to be updated after
              iterations.
          optimize_attr(map): Parameter attributes related with optimizing.
              Currently, it only contains 'learning_rate'.
              Default: {'learning_rate': 1.0}
          regularizer(WeightDecayRegularizer): The Regularizer which will
              be applied on the parameter. Default: None
          do_model_average(bool): True if the model average strategy will
              be applied on this parameter.
          need_clip (bool): Whether the parameter gradient need to be cliped 
              in optimizer. Default is True.
      """
  
      def __init__(self,
                   block,
                   shape,
                   dtype,
                   type=core.VarDesc.VarType.LOD_TENSOR,
                   **kwargs):
          if shape is None:
              raise ValueError("The shape of Parameter should not be None")
          if dtype is None:
              raise ValueError("The dtype of Parameter should not be None")
  
          if len(shape) == 0:
              raise ValueError(
                  "The dimensions of shape for Parameter must be greater than 0")
  
          for each in shape:
              if each < 0:
                  raise ValueError(
                      "Each dimension of shape for Parameter must be greater than 0, but received %s"
                      % list(shape))
  
          Variable.__init__(
              self,
              block,
              persistable=True,
              shape=shape,
              dtype=dtype,
              type=type,
              **kwargs)
          self.trainable = kwargs.get('trainable', True)
  
          self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
  
          self.regularizer = kwargs.get('regularizer', None)
  
          self.do_model_average = kwargs.get('do_model_average', None)
  
          self.need_clip = kwargs.get('need_clip', True)
  
          self.is_distributed = False
  
      def __str__(self):
          return self._to_readable_code()
  
      def to_string(self, throw_on_error, with_details=False):
          """
          To debug string.
  
          Args:
              throw_on_error(bool): raise exception when self is not initialized
                  when throw_on_error is True
              with_details(bool): more details about variables and parameters
                  (e.g. trainable, optimize_attr, ...) will be printed when with_details is True
  
          Returns(str): The debug string.
  
          Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
  
                  prog = fluid.default_main_program()
                  rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32')
                  debug_str = prog.to_string(throw_on_error=True, with_details=False)
                  print(debug_str)
          """
          assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                 bool)
          if with_details:
              res_str = Variable.to_string(self, throw_on_error, True)
              additional_attr = ("trainable", "optimize_attr", "regularizer",
                                 "do_model_average", "need_clip")
              for attr_name in additional_attr:
                  res_str += "%s: %s\n" % (attr_name,
                                           cpt.to_text(getattr(self, attr_name)))
          else:
              res_str = Variable.to_string(self, throw_on_error, False)
          return res_str
  
      __repr__ = __str__
  
  
  class ParamBase(core.VarBase):
      """
      ParamBase is derived from Tensor( Which is the concept in Dygraph Mode). 
      A ParamBase is a persistable Tensor, and will be updated by optimizers 
      after each iteration.
      The training of a neural network is essentially the updating of
      its ParamBase.
  
      Relative to a general Tensor, a ParamBase has several its own
      member variables:
  
      Args:
          trainable(bool): True if the ParamBase need to be updated after
              iterations.
          optimize_attr(map): ParamBase attributes related with optimizing.
              Currently, it only contains 'learning_rate'.
              Default: {'learning_rate': 1.0}
          regularizer(WeightDecayRegularizer): The Regularizer which will
              be applied on the ParamBase. Default: None
          do_model_average(bool): True if the model average strategy will
              be applied on this ParamBase.
          need_clip (bool): Whether the parameter gradient need to be cliped 
              in optimizer. Default is True.
      """
  
      @dygraph_only
      def __init__(self, shape, dtype, **kwargs):
          if shape is None:
              raise ValueError("The shape of Parameter should not be None")
          if dtype is None:
              raise ValueError("The dtype of Parameter should not be None")
  
          if len(shape) == 0:
              raise ValueError(
                  "The dimensions of shape for Parameter must be greater than 0")
  
          for each in shape:
              if each < 0:
                  raise ValueError(
                      "Each dimension of shape for Parameter must be greater than 0, but received %s"
                      % list(shape))
  
          if dtype is not None:
              if not isinstance(dtype, core.VarDesc.VarType):
                  dtype = convert_np_dtype_to_dtype_(dtype)
  
          name = kwargs.get('name', unique_name.generate('_param_base'))
  
          super(ParamBase, self).__init__(dtype
                                          if dtype else core.VarDesc.VarType.FP32,
                                          list(shape) if shape else [], name,
                                          core.VarDesc.VarType.LOD_TENSOR, True)
  
          trainable = kwargs.get('trainable', True)
          self.stop_gradient = not trainable
  
          self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
  
          self.regularizer = kwargs.get('regularizer', None)
  
          self.do_model_average = kwargs.get('do_model_average', None)
  
          self.need_clip = kwargs.get('need_clip', True)
  
          self.is_distributed = False
          # self.block = default_main_program().global_block()
  
      @property
      def trainable(self):
          return not self.stop_gradient
  
      @trainable.setter
      def trainable(self, trainable):
          if isinstance(trainable, bool):
              self.stop_gradient = not trainable
          else:
              raise ValueError(
                  "The type of trainable MUST be bool, but the type is ",
                  type(trainable))
  
      def __str__(self):
          """
          Convert a ParamBase object to a readable string.
  
          Returns(str): A readable string.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  linear = paddle.nn.Linear(3, 3)
                  print(linear.weight)
                  # Parameter containing:
                  # Tensor(shape=[3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
                  #        [[ 0.48948765,  0.05829060, -0.25524026],
                  #         [-0.70368278,  0.52986908, -0.68742192],
                  #         [-0.54217887,  0.48439729,  0.34082305]])
          """
          return "Parameter containing:\n{tensor}".format(
              tensor=super(ParamBase, self).__str__())
  
      def __deepcopy__(self, memo):
          """
          Deep copy parameter, it will always performs Tensor copy.
  
          Examples:
              .. code-block:: python
  
                  import paddle
                  import copy
                  linear = paddle.nn.Linear(1, 3)
                  linear_copy = copy.deepcopy(linear)
  
                  print(linear.weight)
                  # Parameter containing:
                  # Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
                  #     [[-0.30929261, -0.90929240, -1.07851017]])
  
                  print(linear_copy.weight)
                  # Parameter containing:
                  # Tensor(shape=[1, 3], dtype=float32, place=CPUPlace, stop_gradient=False,
                  #     [[-0.30929261, -0.90929240, -1.07851017]])
  
          """
          state = copy.deepcopy(self.__dict__, memo)
          state["name"] = self.name + unique_name.generate("_deepcopy")
          new_param = ParamBase(self.shape, self.dtype, **state)
          memo[id(self)] = new_param
          new_param.copy_(self, True)
          return new_param
  
      __repr__ = __str__
  
  
  # program is a global instance.
  _main_program_ = Program()
  _startup_program_ = Program()
  
  
  def default_startup_program():
      """
      Get default/global startup program.
  
      The :code:`paddle.nn` function will append the initialization operators into startup program.
      The :code:`startup_program` will initialize the parameters by the OPs. 
  
      This method will return the default or the current startup program. Users can use
      :ref:`api_paddle_fluid_framework_program_guard`  to switch :ref:`api_paddle_fluid_framework_Program` .
  
      Returns:
          Program: current default startup program.
  
      Returns type: 
  
      Examples:
          .. code-block:: python
  
              import paddle
  
              paddle.enable_static()
              x = paddle.static.data(name="x", shape=[-1, 784], dtype='float32')
              out = paddle.static.nn.fc(name="fc", x=x, size=10, activation="relu")
              print("main program is: {}".format(paddle.static.default_main_program()))
              print("start up program is: {}".format(paddle.static.default_startup_program()))
      """
      return _startup_program_
  
  
  def default_main_program():
      """
      This API can be used to get ``default main program`` which store the 
      descriptions of Ops and tensors.
  
      For example ``z = paddle.add(x, y)`` will create a new ``add`` 
      Op and a new ``z`` tensor, and they will be recorded in ``default main program`` . 
  
      The ``default main program`` is the default value for ``Program`` parameter in 
      a lot of APIs. For example, the :code:`Executor.run()` will execute the
      :code:`default_main_program` when the program is not specified.
  
      If you want to switch the ``default main program``, you can use :ref:`api_paddle_fluid_framework_program_guard` .
  
      Returns:
          Program: A ``Program`` which holding the descriptions of OPs and tensors in the network.
  
      Examples:
          ..  code-block:: python
  
              import paddle
  
              paddle.enable_static()
              # Sample Network:
              x = paddle.static.data(name='x', shape=[100, 100], dtype='float32')
              y = paddle.static.data(name='x', shape=[100, 100], dtype='float32')
              out = paddle.add(x, y)
  
              #print the number of blocks in the program, 1 in this case
              print(paddle.static.default_main_program().num_blocks) # 1
              #print the default_main_program
              print(paddle.static.default_main_program())
      """
      return _main_program_
  
  
  def switch_main_program(program):
      """
      Switch the main program to a new program.
  
      Args:
          program(Program): The new main program
  
      Returns:
          Program: The previous main program
      """
      global _main_program_
      prev_program = _main_program_
      _main_program_ = program
      return prev_program
  
  
  def switch_startup_program(program):
      """
      Switch the startup program to a new program
      Args:
          program(Program): The new startup program
  
      Returns:
          Program: The previous startup program
      """
      global _startup_program_
      prev_program = _startup_program_
      _startup_program_ = program
      return prev_program
  
  
  @signature_safe_contextmanager
  def program_guard(main_program, startup_program=None):
      """
      :api_attr: Static Graph
  
      Change the global main program and startup program with ``with`` statement.
      Layer functions in the Python ``with`` block will append operators and
      Tensors to the new main programs.
  
      Args:
          main_program(Program): New main program inside ``with`` statement.
          startup_program(Program, optional): New startup program inside ``with`` 
              statement. :code:`None` means not changing startup program, 
              default_startup_program is still used.
              Default: None.
  
      Examples:
         .. code-block:: python
  
            import paddle
  
            paddle.enable_static()
            main_program = paddle.static.Program()
            startup_program = paddle.static.Program()
            with paddle.static.program_guard(main_program, startup_program):
                data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
                hidden = paddle.static.nn.fc(x=data, size=10, activation='relu')
  
      Notes: The temporary :code:`Program` can be used if the user does not need
      to construct either of startup program or main program.
  
      Examples:
         .. code-block:: python
  
            import paddle
  
            paddle.enable_static()
            main_program = paddle.static.Program()
            # does not care about startup program. Just pass a temporary value.
            with paddle.static.program_guard(main_program, paddle.static.Program()):
                data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
  
      """
      from .data_feeder import check_type
      check_type(main_program, 'main_program', Program,
                 'paddle.static.program_guard')
      main_program = switch_main_program(main_program)
      if startup_program is not None:
          check_type(startup_program, 'startup_program', Program,
                     'paddle.static.program_guard')
          startup_program = switch_startup_program(startup_program)
      try:
          yield
      finally:
          switch_main_program(main_program)
          if startup_program is not None:
              switch_startup_program(startup_program)
  
  
  def _get_var(name, program=None):
      """
      Get a variable by name from the global block of a program.
  
      Args:
          name(str): name of the variable
          program(Program|None): program object.
          If None, default_global_program() will be used.
  
      Returns:
          Variable
      """
      if program is None:
          program = default_main_program()
      assert isinstance(name, str)
      assert isinstance(program, Program)
  
      return program.global_block().var(name)
  
  
  @signature_safe_contextmanager
  def _dygraph_guard(tracer):
      global _dygraph_tracer_
      tmp_tracer = _dygraph_tracer_
      _dygraph_tracer_ = tracer
      core._switch_tracer(tracer)
  
      try:
          yield
      finally:
          core._switch_tracer(tmp_tracer)
          _dygraph_tracer_ = tmp_tracer
  
  
  @signature_safe_contextmanager
  def _dygraph_place_guard(place):
      global _global_expected_place_
      tmp_place = _global_expected_place_
      _global_expected_place_ = place
  
      _set_dygraph_tracer_expected_place(place)
  
      try:
          yield
      finally:
          _global_expected_place_ = tmp_place
          _set_dygraph_tracer_expected_place(tmp_place)
  
  
  def switch_device(device):
      global _current_device
      pre_device = _current_device
      _current_device = device
      return pre_device
  
  
  @signature_safe_contextmanager
  def device_guard(device=None):
      """
      **Notes**:
          **The API only supports static mode.**
  
      A context manager that specifies the device on which the OP will be placed.
  
      Args:
          device(str|None): Specify the device to use in the context. It should be ``cpu``,
              ``gpu`` or ``gpu:x``, where ``x`` is the index of the GPUs. 
              When it is set to 'cpu' or 'gpu', all OPs created in the context will be
              placed on CPUPlace or CUDAPlace. When 'gpu' is set and the program runs on
              single-card, the device index will be the same as the device on which the
              executor runs. Default: None, OPs in this context will be automatically
              assigned devices.
  
      Examples:
          .. code-block:: python
  
              import paddle
  
              paddle.enable_static()
              support_gpu = paddle.is_compiled_with_cuda()
              place = paddle.CPUPlace()
              if support_gpu:
                  place = paddle.CUDAPlace(0)
  
              # if GPU is supported, the three OPs below will be automatically assigned to CUDAPlace(0)
              data1 = paddle.full(shape=[1, 3, 8, 8], fill_value=0.5, dtype='float32')
              data2 = paddle.full(shape=[1, 3, 64], fill_value=0.5, dtype='float32')
              shape = paddle.shape(data2)
  
              with paddle.static.device_guard("cpu"):
                  # Ops created here will be placed on CPUPlace
                  shape = paddle.slice(shape, axes=[0], starts=[0], ends=[4])
              with paddle.static.device_guard('gpu'):
                  # if GPU is supported, OPs created here will be placed on CUDAPlace(0), otherwise on CPUPlace
                  out = paddle.reshape(data1, shape=shape)
  
              exe = paddle.static.Executor(place)
              exe.run(paddle.static.default_startup_program())
              result = exe.run(fetch_list=[out])
      """
  
      index = None
      if device and ':' in device:
          device, index = device.split(':')
          if device == 'cpu':
              raise ValueError("Should not set device id for cpu.")
      if device not in ['cpu', 'gpu', 'npu', '', None]:
          raise ValueError(
              "The Attr(device) should be 'cpu' 'npu' or 'gpu', and it can also be empty string or None "
              "when there is no need to specify device. But received %s" % device)
      if index:
          device = ":".join([device, index])
      pre_device = switch_device(device)
      try:
          yield
      finally:
          switch_device(pre_device)
  
  
  def set_flags(flags):
      """
      This function sets the GFlags value in Paddle.
  
      Args:
          flags (dict): A dict contains flags and its value.
  
      Examples:
              .. code-block:: python
  
                  import paddle.fluid as fluid
                  fluid.set_flags({'FLAGS_eager_delete_tensor_gb': 1.0})
      """
      if not isinstance(flags, dict):
          raise TypeError('flags in set_flags should be a dict')
      for key, value in flags.items():
          if core.globals().is_public(key):
              core.globals()[key] = value
          else:
              raise ValueError(
                  "Flag %s cannot set its value through this function." % (key))
  
  
  def get_flags(flags):
      """
      This function gets the GFlags value in Paddle.
  
      Args:
          flags(list|tuple|str): A list/tuple of string or a string which is the flag's name.
  
      Returns:
          flag's value in Paddle.
  
      Examples:
          .. code-block:: python
  
              import paddle.fluid as fluid
  
              flags = ['FLAGS_eager_delete_tensor_gb', 'FLAGS_check_nan_inf']
              res = fluid.get_flags(flags)
              print(res)
              # {'FLAGS_eager_delete_tensor_gb': 0.0, 'FLAGS_check_nan_inf': False}
      """
      flags_value = {}
      if isinstance(flags, (list, tuple)):
          for key in flags:
              if (core.globals().is_public(key)):
                  value = core.globals()[key]
                  temp = {key: value}
                  flags_value.update(temp)
              else:
                  raise ValueError(
                      'Flag %s cannot get its value through this function.' %
                      (key))
      elif isinstance(flags, str):
          if (core.globals().is_public(flags)):
              value = core.globals()[flags]
              temp = {flags: value}
              flags_value.update(temp)
          else:
              raise ValueError(
                  'Flag %s cannot get its value through this function.' % (flags))
      else:
          raise TypeError('Flags in get_flags should be a list, tuple or string.')
      return flags_value
  
  
  def _get_paddle_place(place):
      "convert the string to paddle Place"
      if place is None:
          return place
      if isinstance(place, (core.Place, core.XPUPlace, core.CPUPlace,
                            core.CUDAPinnedPlace, core.CUDAPlace, core.NPUPlace)):
          return place
  
      if not isinstance(place, str):
          raise ValueError(
              "place only support string which is 'Place' and so on.")
  
      place = place.lower()
      if (place == "cpu"):
          return core.CPUPlace()
  
      if (place == "device"):
          return core.Place()
  
      # GPU
      avaliable_gpu_place = re.match(r'gpu:\d+', place)
      if place == "gpu_pinned" or place == "gpu" or avaliable_gpu_place:
          if not core.is_compiled_with_cuda():
              raise ValueError(
                  "The device should not be {}, since PaddlePaddle is " \
                  "not compiled with CUDA".format(avaliable_gpu_place))
          if place == "gpu_pinned":
              return core.CUDAPinnedPlace()
          elif place == "gpu":
              return core.CUDAPlace(0)
          else:
              place_info_list = place.split(':', 1)
              device_id = place_info_list[1]
              device_id = int(device_id)
              return core.CUDAPlace(device_id)
  
      # XPU
      avaliable_xpu_place = re.match(r'xpu:\d+', place)
      if avaliable_xpu_place:
          if not core.is_compiled_with_xpu():
              raise ValueError(
                  "The device should not be {}, since PaddlePaddle is " \
                  "not compiled with XPU".format(avaliable_xpu_place))
          place_info_list = place.split(':', 1)
          device_id = place_info_list[1]
          device_id = int(device_id)
          return core.XPUPlace(device_id)
  
      # NPU
      avaliable_npu_place = re.match(r'npu:\d+', place)
      if avaliable_npu_place:
          if not core.is_compiled_with_npu():
              raise ValueError(
                  "The device should not be {}, since PaddlePaddle is " \
                  "not compiled with NPU".format(avaliable_npu_place))
          place_info_list = place.split(':', 1)
          device_id = place_info_list[1]
          device_id = int(device_id)
          return core.NPUPlace(device_id)
  
      raise ValueError(
          "Paddle supports CPUPlace, CUDAPlace,CUDAPinnedPlace, XPUPlace and NPUPlace, but received {}.".
          format(place))
  
  
  def _get_paddle_place_list(places):
  
      if not isinstance(places, (list, tuple)):
          raise TypeError("places must to be List or Tuple")
  
      ret = []
      for p in places:
          p = _get_paddle_place(p)
          ret.append(p)
  
      return ret
  