parameters: (self,
                   embedding_fn,
                   start_tokens,
                   end_token,
                   softmax_temperature=None,
                   seed=None)
docstring: r"""
          Constructor of SampleEmbeddingHelper.
  
          Parameters:
              embedding_fn(callable): A functor to apply on the argmax results. 
                  Mostly it is an embedding layer to transform ids to embeddings.
                  **Note that fluid.embedding should be used here rather than
                  fluid.layers.embedding, since shape of ids is [batch_size].
                  when using fluid.layers.embedding, must unsqueeze in embedding_fn.**
              start_tokens(Variable):  A `int64` tensor shaped `[batch_size]`,
                  representing the start tokens.
              end_token(int): The end token id.
              softmax_temperature(float, optional): the value to divide the logits
                  by before computing the softmax. Higher temperatures (above 1.0)
                  lead to more random, while lower temperatures push the sampling
                  distribution towards the argmax. It must be strictly greater than
                  0. Defaults to None, meaning using a temperature valued 1.0.
              seed: (int, optional) The sampling seed. Defaults to None, meaning not
                  to use fixed seed.
  
          Returns:
              tuple: A tuple( :code:`(initial_inputs, initial_states, finished)` ). \
                  `initial_inputs` and `initial_states` both are a (possibly nested \
                  structure of) tensor variable[s], and `finished` is a tensor with \
                  bool data type.
docstring_summary: r"""
          Constructor of SampleEmbeddingHelper.
docstring_tokens: r Constructor of SampleEmbeddingHelper .
function: def __init__(self,
                   embedding_fn,
                   start_tokens,
                   end_token,
                   softmax_temperature=None,
                   seed=None):
          r"""
          Constructor of SampleEmbeddingHelper.
  
          Parameters:
              embedding_fn(callable): A functor to apply on the argmax results. 
                  Mostly it is an embedding layer to transform ids to embeddings.
                  **Note that fluid.embedding should be used here rather than
                  fluid.layers.embedding, since shape of ids is [batch_size].
                  when using fluid.layers.embedding, must unsqueeze in embedding_fn.**
              start_tokens(Variable):  A `int64` tensor shaped `[batch_size]`,
                  representing the start tokens.
              end_token(int): The end token id.
              softmax_temperature(float, optional): the value to divide the logits
                  by before computing the softmax. Higher temperatures (above 1.0)
                  lead to more random, while lower temperatures push the sampling
                  distribution towards the argmax. It must be strictly greater than
                  0. Defaults to None, meaning using a temperature valued 1.0.
              seed: (int, optional) The sampling seed. Defaults to None, meaning not
                  to use fixed seed.
  
          Returns:
              tuple: A tuple( :code:`(initial_inputs, initial_states, finished)` ). \
                  `initial_inputs` and `initial_states` both are a (possibly nested \
                  structure of) tensor variable[s], and `finished` is a tensor with \
                  bool data type.
          """
          super(SampleEmbeddingHelper, self).__init__(embedding_fn, start_tokens,
                                                      end_token)
          self.softmax_temperature = tensor.fill_constant(
              shape=[1], dtype="float32", value=softmax_temperature
          ) if softmax_temperature is not None else None
          self.seed = seed
function_tokens: def __init__ ( self , embedding_fn , start_tokens , end_token , softmax_temperature = None , seed = None ) : super ( SampleEmbeddingHelper , self ) . __init__ ( embedding_fn , start_tokens , end_token ) self . softmax_temperature = tensor . fill_constant ( shape = [ 1 ] , dtype = "float32" , value = softmax_temperature ) if softmax_temperature is not None else None self . seed = seed