parameters: (self,
                                   inputs,
                                   weight_collections=None,
                                   trainable=None)
return_statement: return embedding_ops.safe_embedding_lookup_sparse(
          embedding_weights=embedding_weights,
          sparse_ids=sparse_ids,
          sparse_weights=sparse_weights,
          combiner=self.combiner,
          name='%s_weights' % self.name,
          max_norm=self.max_norm)
docstring: Private method that follows the signature of _get_dense_tensor.
docstring_summary: Private method that follows the signature of _get_dense_tensor.
docstring_tokens: Private method that follows the signature of _get_dense_tensor .
function: def _get_dense_tensor_internal(self,
                                   inputs,
                                   weight_collections=None,
                                   trainable=None):
      """Private method that follows the signature of _get_dense_tensor."""
      # Get sparse IDs and weights.
      sparse_tensors = self.categorical_column._get_sparse_tensors(  # pylint: disable=protected-access
          inputs, weight_collections=weight_collections, trainable=trainable)
      sparse_ids = sparse_tensors.id_tensor
      sparse_weights = sparse_tensors.weight_tensor
  
      embedding_weights = self.layer_creator(
          weight_collections=weight_collections,
          scope=variable_scope.get_variable_scope())
  
      if self.ckpt_to_load_from is not None:
        to_restore = embedding_weights
        if isinstance(to_restore, variables.PartitionedVariable):
          to_restore = to_restore._get_variable_list()  # pylint: disable=protected-access
        checkpoint_utils.init_from_checkpoint(self.ckpt_to_load_from, {
            self.tensor_name_in_ckpt: to_restore
        })
  
      # Return embedding lookup result.
      return embedding_ops.safe_embedding_lookup_sparse(
          embedding_weights=embedding_weights,
          sparse_ids=sparse_ids,
          sparse_weights=sparse_weights,
          combiner=self.combiner,
          name='%s_weights' % self.name,
          max_norm=self.max_norm)
function_tokens: def _get_dense_tensor_internal ( self , inputs , weight_collections = None , trainable = None ) : # Get sparse IDs and weights. sparse_tensors = self . categorical_column . _get_sparse_tensors ( # pylint: disable=protected-access inputs , weight_collections = weight_collections , trainable = trainable ) sparse_ids = sparse_tensors . id_tensor sparse_weights = sparse_tensors . weight_tensor embedding_weights = self . layer_creator ( weight_collections = weight_collections , scope = variable_scope . get_variable_scope ( ) ) if self . ckpt_to_load_from is not None : to_restore = embedding_weights if isinstance ( to_restore , variables . PartitionedVariable ) : to_restore = to_restore . _get_variable_list ( ) # pylint: disable=protected-access checkpoint_utils . init_from_checkpoint ( self . ckpt_to_load_from , { self . tensor_name_in_ckpt : to_restore } ) # Return embedding lookup result. return embedding_ops . safe_embedding_lookup_sparse ( embedding_weights = embedding_weights , sparse_ids = sparse_ids , sparse_weights = sparse_weights , combiner = self . combiner , name = '%s_weights' % self . name , max_norm = self . max_norm )