function fitLoop(_x, _x2, _x3, _x4, _x5, _x6, _x7, _x8, _x9, _x10, _x11, _x12, _x13, _x14, _x15) {
	  return _fitLoop.apply(this, arguments);
	}
Abstract fit function for `f(ins)`.
@param f A Function returning a list of tensors. For training, this
  function is expected to perform the updates to the variables.
@param ins List of tensors to be fed to `f`.
@param outLabels List of strings, display names of the outputs of `f`.
@param batchSize Integer batch size or `== null` if unknown. Default : 32.
@param epochs Number of times to iterate over the data. Default : 1.
@param verbose Verbosity mode: 0, 1, or 2. Default: 1.
@param callbacks List of callbacks to be called during training.
@param valF Function to call for validation.
@param valIns List of tensors to be fed to `valF`.
@param shuffle Whether to shuffle the data at the beginning of every
epoch. Default : true.
@param callbackMetrics List of strings, the display names of the metrics
  passed to the callbacks. They should be the concatenation of the
  display names of the outputs of `f` and the list of display names
  of the outputs of `valF`.
@param initialEpoch Epoch at which to start training (useful for
  resuming a previous training run). Default : 0.
@param stepsPerEpoch Total number of steps (batches on samples) before
  declaring one epoch finished and starting the next epoch. Ignored with
  the default value of `undefined` or `null`.
@param validationSteps Number of steps to run validation for (only if
  doing validation from data tensors). Not applicable for tfjs-layers.
@returns A `History` object.