Added nb_epochs to grid_search

      valid_dataset: Dataset,

      metric: Metric,

      output_transformers: List[Transformer] = [],

      nb_epoch: int = 5,

      use_max: bool = True,

      logdir: Optional[str] = None,

      **kwargs,

      `train_dataset` and `valid_dataset` may have been transformed

      for learning and need the transform to be inverted before

      the metric can be evaluated on a model.

    nb_epoch: int, (default 5)

    use_max: bool, optional

      If True, return the model with the highest score. Else return

      model with the minimum score.

        model_dir = tempfile.mkdtemp()

      model_params['model_dir'] = model_dir

      model = self.model_builder(**model_params)

      model.fit(train_dataset)

      model.fit(train_dataset, nb_epoch=nb_epoch)

      try:

        model.save()

      # Some models autosave