parameters: (self, features, feature_columns, is_training)
return_statement: return logits
docstring: See base class.
docstring_summary: See base class.
docstring_tokens: See base class .
function: def build_model(self, features, feature_columns, is_training):
      """See base class."""
      self._feature_columns = feature_columns
      partitioner = partitioned_variables.min_max_variable_partitioner(
          max_partitions=self._num_ps_replicas, min_slice_size=64 << 20)
      with variable_scope.variable_scope(
          self._scope, values=features.values(),
          partitioner=partitioner) as scope:
        if self._joint_weights:
          logits, _, _ = layers.joint_weighted_sum_from_feature_columns(
              columns_to_tensors=features,
              feature_columns=self._get_feature_columns(),
              num_outputs=self._num_label_columns,
              weight_collections=[self._scope],
              trainable=self._trainable,
              scope=scope)
        else:
          logits, _, _ = layers.weighted_sum_from_feature_columns(
              columns_to_tensors=features,
              feature_columns=self._get_feature_columns(),
              num_outputs=self._num_label_columns,
              weight_collections=[self._scope],
              trainable=self._trainable,
              scope=scope)
      return logits
function_tokens: def build_model ( self , features , feature_columns , is_training ) : self . _feature_columns = feature_columns partitioner = partitioned_variables . min_max_variable_partitioner ( max_partitions = self . _num_ps_replicas , min_slice_size = 64 << 20 ) with variable_scope . variable_scope ( self . _scope , values = features . values ( ) , partitioner = partitioner ) as scope : if self . _joint_weights : logits , _ , _ = layers . joint_weighted_sum_from_feature_columns ( columns_to_tensors = features , feature_columns = self . _get_feature_columns ( ) , num_outputs = self . _num_label_columns , weight_collections = [ self . _scope ] , trainable = self . _trainable , scope = scope ) else : logits , _ , _ = layers . weighted_sum_from_feature_columns ( columns_to_tensors = features , feature_columns = self . _get_feature_columns ( ) , num_outputs = self . _num_label_columns , weight_collections = [ self . _scope ] , trainable = self . _trainable , scope = scope ) return logits