parameters: (self,
                        input_tensor,
                        num_outputs=1,
                        weight_collections=None,
                        trainable=True)
return_statement: return _create_embedding_lookup(
          input_tensor=self.to_sparse_tensor(input_tensor),
          weight_tensor=None,
          vocab_size=self.length * self.source_column.dimension,
          dimension=num_outputs,
          weight_collections=_add_variable_collection(weight_collections),
          initializer=init_ops.zeros_initializer,
          combiner="sum",
          trainable=trainable,
          name=self.name)
docstring: Returns a Tensor as linear predictions and a list of created Variable.
docstring_summary: Returns a Tensor as linear predictions and a list of created Variable.
docstring_tokens: Returns a Tensor as linear predictions and a list of created Variable .
function: def to_weighted_sum(self,
                        input_tensor,
                        num_outputs=1,
                        weight_collections=None,
                        trainable=True):
      """Returns a Tensor as linear predictions and a list of created Variable."""
      return _create_embedding_lookup(
          input_tensor=self.to_sparse_tensor(input_tensor),
          weight_tensor=None,
          vocab_size=self.length * self.source_column.dimension,
          dimension=num_outputs,
          weight_collections=_add_variable_collection(weight_collections),
          initializer=init_ops.zeros_initializer,
          combiner="sum",
          trainable=trainable,
          name=self.name)
function_tokens: def to_weighted_sum ( self , input_tensor , num_outputs = 1 , weight_collections = None , trainable = True ) : return _create_embedding_lookup ( input_tensor = self . to_sparse_tensor ( input_tensor ) , weight_tensor = None , vocab_size = self . length * self . source_column . dimension , dimension = num_outputs , weight_collections = _add_variable_collection ( weight_collections ) , initializer = init_ops . zeros_initializer , combiner = "sum" , trainable = trainable , name = self . name )