parameters: (input,
                          size,
                          is_sparse=False,
                          is_distributed=False,
                          padding_idx=None,
                          param_attr=None,
                          dtype='float32')
return_statement: return fluid.layers.nn._pull_sparse_v2(
          input=input,
          size=size,
          table_id=FLEET_GLOBAL_DICT["emb_to_table"][name],
          accessor_class=FLEET_GLOBAL_DICT["emb_to_accessor"][name],
          name=name,
          ctr_label_name=FLEET_GLOBAL_DICT["click_name"],
          padding_id=padding_idx,
          dtype=dtype,
          scale_sparse_grad=FLEET_GLOBAL_DICT["scale_sparse_grad"])
docstring: add fleet embedding v2, this interface is not for users.
      Args:
          input(Variable|list of Variable): Input is a Tensor<int64> Variable
          size(list of int): the embedding dim
          is_sparse(bool): whether input is sparse ids
          is_distributed(bool): whether in distributed mode
          padding_idx(int): padding idx of input
          param_attr(ParamAttr): To specify the weight parameter property
          dtype(str): data type of output
docstring_summary: add fleet embedding v2, this interface is not for users.
      Args:
          input(Variable|list of Variable): Input is a Tensor<int64> Variable
          size(list of int): the embedding dim
          is_sparse(bool): whether input is sparse ids
          is_distributed(bool): whether in distributed mode
          padding_idx(int): padding idx of input
          param_attr(ParamAttr): To specify the weight parameter property
          dtype(str): data type of output
docstring_tokens: add fleet embedding v2 this interface is not for users . Args : input ( Variable|list of Variable ) : Input is a Tensor<int64 > Variable size ( list of int ) : the embedding dim is_sparse ( bool ) : whether input is sparse ids is_distributed ( bool ) : whether in distributed mode padding_idx ( int ) : padding idx of input param_attr ( ParamAttr ) : To specify the weight parameter property dtype ( str ) : data type of output
function: def _fleet_embedding_v2(input,
                          size,
                          is_sparse=False,
                          is_distributed=False,
                          padding_idx=None,
                          param_attr=None,
                          dtype='float32'):
      """
      add fleet embedding v2, this interface is not for users.
      Args:
          input(Variable|list of Variable): Input is a Tensor<int64> Variable
          size(list of int): the embedding dim
          is_sparse(bool): whether input is sparse ids
          is_distributed(bool): whether in distributed mode
          padding_idx(int): padding idx of input
          param_attr(ParamAttr): To specify the weight parameter property
          dtype(str): data type of output
      """
      # check and set params
      _prepare_params(input, size, is_sparse, is_distributed, padding_idx,
                      param_attr, dtype)
      name = param_attr.name
      size = size[-1]
      if padding_idx is None:
          padding_idx = 0
  
      return fluid.layers.nn._pull_sparse_v2(
          input=input,
          size=size,
          table_id=FLEET_GLOBAL_DICT["emb_to_table"][name],
          accessor_class=FLEET_GLOBAL_DICT["emb_to_accessor"][name],
          name=name,
          ctr_label_name=FLEET_GLOBAL_DICT["click_name"],
          padding_id=padding_idx,
          dtype=dtype,
          scale_sparse_grad=FLEET_GLOBAL_DICT["scale_sparse_grad"])
function_tokens: def _fleet_embedding_v2 ( input , size , is_sparse = False , is_distributed = False , padding_idx = None , param_attr = None , dtype = 'float32' ) : # check and set params _prepare_params ( input , size , is_sparse , is_distributed , padding_idx , param_attr , dtype ) name = param_attr . name size = size [ - 1 ] if padding_idx is None : padding_idx = 0 return fluid . layers . nn . _pull_sparse_v2 ( input = input , size = size , table_id = FLEET_GLOBAL_DICT [ "emb_to_table" ] [ name ] , accessor_class = FLEET_GLOBAL_DICT [ "emb_to_accessor" ] [ name ] , name = name , ctr_label_name = FLEET_GLOBAL_DICT [ "click_name" ] , padding_id = padding_idx , dtype = dtype , scale_sparse_grad = FLEET_GLOBAL_DICT [ "scale_sparse_grad" ] )