function fit(_x9, _x10, _x11) {
	      return _fit.apply(this, arguments);
	    }
Trains the model for a fixed number of epochs (iterations on a
dataset).

```js
const model = tf.sequential({
    layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
for (let i = 1; i < 5 ; ++i) {
  const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
      batchSize: 4,
      epochs: 3
  });
  console.log("Loss after Epoch " + i + " : " + h.history.loss[0]);
}
```

@param x `tf.Tensor` of training data, or an array of `tf.Tensor`s if the
model has multiple inputs. If all inputs in the model are named, you
can also pass a dictionary mapping input names to `tf.Tensor`s.
@param y `tf.Tensor` of target (label) data, or an array of `tf.Tensor`s if
the model has multiple outputs. If all outputs in the model are named,
you can also pass a dictionary mapping output names to `tf.Tensor`s.
@param args A `ModelFitArgs`, containing optional fields.

@return A `History` instance. Its `history` attribute contains all
  information collected during training.

@exception ValueError In case of mismatch between the provided input
data and what the model expects.

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