message: adjusted line lengths to be <80
diff: @@ -502,12 +502,12 @@ class Model(base_layer.Layer, version_utils.ModelVersionSelector):
  y_pred = predicted values with shape = `[batch_size, d0, .. dN]`. It
  returns a weighted loss float tensor. If a custom `Loss` instance is
  used and reduction is set to `None`, return value has the shape
  - `[batch_size, d0, .. dN-1]` i.e. per-sample or per-timestep loss values;
  - otherwise, it is a scalar. If the model has multiple outputs, you can
  - use a different loss on each output by passing a dictionary or a list
  - of losses. The loss value that will be minimized by the model will
  - then be the sum of all individual losses, unless `loss_weights` is
  - specified.
  + `[batch_size, d0, .. dN-1]` i.e. per-sample or per-timestep loss
  + values; otherwise, it is a scalar. If the model has multiple outputs,
  + you can use a different loss on each output by passing a dictionary
  + or a list of losses. The loss value that will be minimized by the
  + model will then be the sum of all individual losses, unless
  + `loss_weights` is specified.
  metrics: List of metrics to be evaluated by the model during training
  and testing. Each of this can be a string (name of a built-in
  function), function or a `tf.keras.metrics.Metric` instance. See
  