class_docstr: A container class for losses passed to `Model.compile`.
class_docstr_tok: ["A","container","class","for","losses","passed","to","`","Model.compile","`","."]
docstr: Creates per-output loss metrics, but only for multi-output Models.
code: def _create_metrics(self):
      """Creates per-output loss metrics, but only for multi-output Models."""
      if len(self._output_names) == 1:
          self._per_output_metrics = [None]
      else:
          self._per_output_metrics = []
          for loss_obj, output_name in zip(
              self._losses, self._output_names
          ):
              if loss_obj is None:
                  self._per_output_metrics.append(None)
              else:
                  self._per_output_metrics.append(
                      metrics_mod.Mean(output_name + "_loss")
                  )
  
imports: import copy
  from tensorflow.python.distribute import distribute_lib
  from tensorflow.python.keras import losses
  from tensorflow.python.keras import metrics
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils import losses_utils
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 5 for calling the LossesContainer obj's underlying member method code with example usage: obj._create_metrics() without return types