class_docstr: A container class for losses passed to `Model.compile`.
class_docstr_tok: ["A","container","class","for","losses","passed","to","`","Model.compile","`","."]
code: def __init__(self, losses, loss_weights=None, output_names=None):
      super(LossesContainer, self).__init__(output_names=output_names)
  
      # Keep user-supplied values untouched for recompiling and serialization.
      self._user_losses = losses
      self._user_loss_weights = loss_weights
  
      self._losses = losses
      self._loss_weights = loss_weights
      self._per_output_metrics = (
          None  # Per-output losses become metrics.
      )
      self._loss_metric = metrics_mod.Mean(name="loss")  # Total loss.
      self._built = False
  
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 1 to create a new LossesContainer object from inherited base classes: Container with example: obj = LossesContainer(losses, loss_weights, output_names)