class_docstr: A container class for metrics passed to `Model.compile`.
class_docstr_tok: ["A","container","class","for","metrics","passed","to","`","Model.compile","`","."]
docstr: Cache the flat order needed when returning metrics, for backwards compat.
code: def _create_ordered_metrics(self):
      """Cache the flat order needed when returning metrics, for backwards compat."""
      self._metrics_in_order = []
      for output_metrics, output_weighted_metrics in zip(
          self._metrics, self._weighted_metrics
      ):
          for m in nest.flatten(output_metrics):
              if m is not None:
                  self._metrics_in_order.append(m)
          for wm in nest.flatten(output_weighted_metrics):
              if wm is not None:
                  self._metrics_in_order.append(wm)
  
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 8 for calling the MetricsContainer obj's underlying member method code with example usage: obj._create_ordered_metrics() without return types