function trainOnBatch(_x14, _x15) {
	      return _trainOnBatch.apply(this, arguments);
	    }
Runs a single gradient update on a single batch of data.

This method differs from `fit()` and `fitDataset()` in the following
regards:
  - It operates on exactly one batch of data.
  - It returns only the loss and matric values, instead of
    returning the batch-by-batch loss and metric values.
  - It doesn't support fine-grained options such as verbosity and
    callbacks.

@param x Input data. It could be one of the following:
  - A `tf.Tensor`, or an Array of `tf.Tensor`s (in case the model has
    multiple inputs).
  - An Object mapping input names to corresponding `tf.Tensor` (if the
    model has named inputs).
@param y Target darta. It could be either a `tf.Tensor` a multiple
  `tf.Tensor`s. It should be consistent with `x`.
@returns Training loss or losses (in case the model has
  multiple outputs), along with metrics (if any), as numbers.

@doc {heading: 'Models', subheading: 'Classes'}