message: Update the docstring for validation_step wrt to partially consumed dataset.
diff: @@ -667,7 +667,7 @@ class Model(network.Network):
  - tuple `(x_val, y_val, val_sample_weights)` of Numpy arrays
  - dataset
  For the first two cases, `batch_size` must be provided.
  - For the last case, `validation_steps` must be provided.
  + For the last case, `validation_steps` could be provided.
  shuffle: Boolean (whether to shuffle the training data
  before each epoch) or str (for 'batch').
  'batch' is a special option for dealing with the
  @@ -709,9 +709,13 @@ class Model(network.Network):
  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_data is a `tf.data` dataset
  - and 'validation_steps' is None, validation
  - will run until the `validation_data` dataset is exhausted.
  + 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.
  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
  