parameters: (self,
                             program=None,
                             dataset=None,
                             scope=None,
                             thread=0,
                             debug=False,
                             fetch_list=None,
                             fetch_info=None,
                             print_period=100,
                             fetch_handler=None)
return_statement: return self._run_from_dataset(program, dataset, scope, thread, True,
                                        debug, fetch_list, fetch_info,
                                        print_period, fetch_handler)
docstring: Infer from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset.
          Given a program, either a program or compiled program, infer_from_dataset will
          consume all data samples in dataset. Input scope can be given by users. By default,
          scope is global_scope(). The total number of thread run in training is `thread`.
          Thread number used in training will be minimum value of threadnum in Dataset and
          the value of thread in this interface. Debug can be set so that executor will display
          Run-Time for all operators and the throughputs of current infer task.
  
          The document of infer_from_dataset is almost the same as train_from_dataset,
          except that in distributed training, push gradients will be disabled in infer_from_dataset.
          infer_from_dataset() can be used for evaluation in multi-threadvery easily.
  
          Args:
              program(Program|CompiledProgram): the program that needs to be run,
                  if not provided, then default_main_program (not compiled) will be used.
              dataset(paddle.fluid.Dataset): dataset created outside this function,
                  a user should provide a well-defined dataset before calling this function.
                  Please check the document of Dataset if needed. default is None
              scope(Scope): the scope used to run this program, you can switch it to different scope
                  for each run. default is global_scope
              thread(int): number of thread a user wants to run in this function. Default is 0, which
                  means using thread num of dataset
              debug(bool): whether a user wants to run infer_from_dataset, default is False
              fetch_list(Tensor List): fetch Tensor list, each Tensor will be printed during
                  training, default is None
              fetch_info(String List): print information for each Tensor, default is None
              print_period(int): the number of mini-batches for each print, default is 100
              fetch_handler(FetchHandler): a user define class for fetch output.
  
          Returns:
              None
  
          Examples:
  
              .. code-block:: python
  
                  import paddle
  
                  paddle.enable_static()
                  place = paddle.CPUPlace()  # you can set place = paddle.CUDAPlace(0) to use gpu
                  exe = paddle.static.Executor(place)
                  x = paddle.static.data(name="x", shape=[None, 10, 10], dtype="int64")
                  y = paddle.static.data(name="y", shape=[None, 1], dtype="int64", lod_level=1)
                  dataset = paddle.fluid.DatasetFactory().create_dataset()
                  dataset.set_use_var([x, y])
                  dataset.set_thread(1)
                  # you should set your own filelist, e.g. filelist = ["dataA.txt"]
                  filelist = []
                  dataset.set_filelist(filelist)
                  exe.run(paddle.static.default_startup_program())
                  exe.infer_from_dataset(program=paddle.static.default_main_program(),
                                         dataset=dataset)
docstring_summary: Infer from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset.
          Given a program, either a program or compiled program, infer_from_dataset will
          consume all data samples in dataset. Input scope can be given by users. By default,
          scope is global_scope(). The total number of thread run in training is `thread`.
          Thread number used in training will be minimum value of threadnum in Dataset and
          the value of thread in this interface. Debug can be set so that executor will display
          Run-Time for all operators and the throughputs of current infer task.
docstring_tokens: Infer from a pre - defined Dataset . Dataset is defined in paddle . fluid . dataset . Given a program either a program or compiled program infer_from_dataset will consume all data samples in dataset . Input scope can be given by users . By default scope is global_scope () . The total number of thread run in training is thread . Thread number used in training will be minimum value of threadnum in Dataset and the value of thread in this interface . Debug can be set so that executor will display Run - Time for all operators and the throughputs of current infer task .
function: def infer_from_dataset(self,
                             program=None,
                             dataset=None,
                             scope=None,
                             thread=0,
                             debug=False,
                             fetch_list=None,
                             fetch_info=None,
                             print_period=100,
                             fetch_handler=None):
          """
          Infer from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset.
          Given a program, either a program or compiled program, infer_from_dataset will
          consume all data samples in dataset. Input scope can be given by users. By default,
          scope is global_scope(). The total number of thread run in training is `thread`.
          Thread number used in training will be minimum value of threadnum in Dataset and
          the value of thread in this interface. Debug can be set so that executor will display
          Run-Time for all operators and the throughputs of current infer task.
  
          The document of infer_from_dataset is almost the same as train_from_dataset,
          except that in distributed training, push gradients will be disabled in infer_from_dataset.
          infer_from_dataset() can be used for evaluation in multi-threadvery easily.
  
          Args:
              program(Program|CompiledProgram): the program that needs to be run,
                  if not provided, then default_main_program (not compiled) will be used.
              dataset(paddle.fluid.Dataset): dataset created outside this function,
                  a user should provide a well-defined dataset before calling this function.
                  Please check the document of Dataset if needed. default is None
              scope(Scope): the scope used to run this program, you can switch it to different scope
                  for each run. default is global_scope
              thread(int): number of thread a user wants to run in this function. Default is 0, which
                  means using thread num of dataset
              debug(bool): whether a user wants to run infer_from_dataset, default is False
              fetch_list(Tensor List): fetch Tensor list, each Tensor will be printed during
                  training, default is None
              fetch_info(String List): print information for each Tensor, default is None
              print_period(int): the number of mini-batches for each print, default is 100
              fetch_handler(FetchHandler): a user define class for fetch output.
  
          Returns:
              None
  
          Examples:
  
              .. code-block:: python
  
                  import paddle
  
                  paddle.enable_static()
                  place = paddle.CPUPlace()  # you can set place = paddle.CUDAPlace(0) to use gpu
                  exe = paddle.static.Executor(place)
                  x = paddle.static.data(name="x", shape=[None, 10, 10], dtype="int64")
                  y = paddle.static.data(name="y", shape=[None, 1], dtype="int64", lod_level=1)
                  dataset = paddle.fluid.DatasetFactory().create_dataset()
                  dataset.set_use_var([x, y])
                  dataset.set_thread(1)
                  # you should set your own filelist, e.g. filelist = ["dataA.txt"]
                  filelist = []
                  dataset.set_filelist(filelist)
                  exe.run(paddle.static.default_startup_program())
                  exe.infer_from_dataset(program=paddle.static.default_main_program(),
                                         dataset=dataset)
  
          """
          return self._run_from_dataset(program, dataset, scope, thread, True,
                                        debug, fetch_list, fetch_info,
                                        print_period, fetch_handler)
function_tokens: def infer_from_dataset ( self , program = None , dataset = None , scope = None , thread = 0 , debug = False , fetch_list = None , fetch_info = None , print_period = 100 , fetch_handler = None ) : return self . _run_from_dataset ( program , dataset , scope , thread , True , debug , fetch_list , fetch_info , print_period , fetch_handler )