function fit(_x10, _x11, _x12) {
	      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'});
const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
  batchSize: 4,
  epochs: 3
});
console.log(history.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 `ModelFitConfig`, 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'}