message: Update model.fit() doc to mention that if an infinitely repeated dataset is passed in, `steps_per_epoch` must be provided.
diff: @@ -615,17 +615,19 @@ class Model(network.Network, version_utils.VersionSelector):
  the batch size, or 1 if that cannot be determined. If x is a
  `tf.data` dataset, and 'steps_per_epoch'
  is None, the epoch will run until the input dataset is exhausted.
  - This argument is not supported with array inputs.
  + When passing an infinitely repeating dataset, you must specify the
  + `steps_per_epoch` argument. This argument is not supported with
  + array inputs.
  validation_steps: Only relevant if `validation_data` is provided and
  is a `tf.data` dataset. Total number of steps (batches of
  samples) to draw before stopping when performing validation
  at the end of every epoch. If 'validation_steps' is None, validation
  will run until the `validation_data` dataset is exhausted. In the
  - case of a infinite dataset, it will run into a infinite loop.
  - If 'validation_steps' is specified and only part of the dataset
  - will be consumed, the evaluation will start from the beginning of
  - the dataset at each epoch. This ensures that the same validation
  - samples are used every time.
  + case of an infinitely repeated dataset, it will run into an
  + infinite loop. If 'validation_steps' is specified and only part of
  + the dataset will be consumed, the evaluation will start from the
  + beginning of the dataset at each epoch. This ensures that the same
  + validation samples are used every time.
  validation_freq: Only relevant if validation data is provided. Integer
  or `collections_abc.Container` instance (e.g. list, tuple, etc.).
  If an integer, specifies how many training epochs to run before a
  