message: There cannot be newlines in Args, Returns, Raises section, because in markdown that's a paragraph and the website render comes out wrong. See
diff: @@ -236,7 +236,6 @@ class Model(network.Network, version_utils.VersionSelector):
  See `tf.keras.optimizers`.
  loss: String (name of objective function), objective function or
  `tf.keras.losses.Loss` instance. See `tf.keras.losses`.
  -
  An objective function is any callable with the signature
  `loss = fn(y_true, y_pred)`, where
  y_true = ground truth values with shape = `[batch_size, d0, .. dN]`,
  @@ -244,23 +243,19 @@ class Model(network.Network, version_utils.VersionSelector):
  where shape = `[batch_size, d0, .. dN-1]`.
  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] ie. 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.
  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 `tf.keras.metrics`.
  Typically you will use `metrics=['accuracy']`. A function is any
  callable with the signature `result = fn(y_true, y_pred)`.
  -
  To specify different metrics for different outputs of a
  multi-output model, you could also pass a dictionary, such as
  `metrics={'output_a': 'accuracy', 'output_b': ['accuracy', 'mse']}`.
  