old_contents: from .. import core
  from ..layer_helper import LayerHelper
  
  __all__ = ['data']
  
  
  def data(name,
           shape,
           append_batch_size=True,
           dtype='float32',
           lod_level=0,
           type=core.VarDesc.VarType.LOD_TENSOR,
           stop_gradient=True):
      """
      Data Layer.
  
      Args:
         name: The name/alias of the function
         shape: Tuple declaring the shape.
         append_batch_size: Whether or not to append the data as a batch.
         dtype: The type of data : float32, float_16, int etc
         type: The output type. By default it is LOD_TENSOR.
         lod_level(int): The LoD Level. 0 means the input data is not a sequence.
         main_program: Name of the main program that calls this
         startup_program: Name of the startup program
         stop_gradient: A boolean that mentions whether gradient should flow.
  
      This function takes in input and based on whether data has
      to be returned back as a minibatch, it creates the global variable using
      the helper functions. The global variables can be accessed by all the
      following operations and layers in the graph.
  
      All the input variables of this function are passed in as local variables
      to the LayerHelper constructor.
  
      """
      helper = LayerHelper('data', **locals())
      shape = list(shape)
      for i in xrange(len(shape)):
          if shape[i] is None:
              shape[i] = -1
              append_batch_size = False
          elif shape[i] < 0:
              append_batch_size = False
  
      if append_batch_size:
          shape = [-1] + shape  # append batch size as -1
  
      return helper.create_global_variable(
          name=name,
          shape=shape,
          dtype=dtype,
          type=type,
          stop_gradient=stop_gradient,
          lod_level=lod_level)
  
new_contents: from .. import core
  from ..layer_helper import LayerHelper
  
  __all__ = ['data']
  
  
  def data(name,
           shape,
           append_batch_size=True,
           dtype='float32',
           lod_level=0,
           type=core.VarDesc.VarType.LOD_TENSOR,
           stop_gradient=True):
      """
      **Data Layer**
  
      This function takes in the input and based on whether data has
      to be returned back as a minibatch, it creates the global variable using
      the helper functions. The global variables can be accessed by all the
      following operations and layers in the graph.
  
      All the input variables of this function are passed in as local variables
      to the LayerHelper constructor.
  
      Args:
         name(str): The name/alias of the function
         shape(list): Tuple declaring the shape.
         append_batch_size(bool): Whether or not to append the data as a batch.
         dtype(int|float): The type of data : float32, float_16, int etc
         type(VarType): The output type. By default it is LOD_TENSOR.
         lod_level(int): The LoD Level. 0 means the input data is not a sequence.
         main_program(Program): Name of the main program that calls this
         startup_program(Program): Name of the startup program
         stop_gradient(bool): A boolean that mentions whether gradient should flow.
  
      Returns:
          Variable: The global variable that gives access to the data.
  
      Examples:
          .. code-block:: python
  
            data = fluid.layers.data(name='x', shape=[784], dtype='float32')
      """
      helper = LayerHelper('data', **locals())
      shape = list(shape)
      for i in xrange(len(shape)):
          if shape[i] is None:
              shape[i] = -1
              append_batch_size = False
          elif shape[i] < 0:
              append_batch_size = False
  
      if append_batch_size:
          shape = [-1] + shape  # append batch size as -1
  
      return helper.create_global_variable(
          name=name,
          shape=shape,
          dtype=dtype,
          type=type,
          stop_gradient=stop_gradient,
          lod_level=lod_level)
  
subject: Add doc for data layer (#6858)