def fit(
        self,
        x=None,
        y=None,
        epochs=None,
        callbacks=None,
        validation_split=0.2,
        validation_data=None,
        **kwargs
    ):
        """Search for the best model and hyperparameters for the AutoModel.

        It will search for the best model based on the performances on
        validation data.

        # Arguments
            x: numpy.ndarray or tensorflow.Dataset. Training data x. The input
                data should be numpy.ndarray or tf.data.Dataset. The data should
                be one dimensional. Each element in the data should be a string
                which is a full sentence.
            y: numpy.ndarray or tensorflow.Dataset. Training data y. It can be
                raw labels, one-hot encoded if more than two classes, or binary
                encoded for binary classification.
            epochs: Int. The number of epochs to train each model during the
                search. If unspecified, by default we train for a maximum of
                1000 epochs, but we stop training if the validation loss stops
                improving for 10 epochs (unless you specified an EarlyStopping
                callback as part of the callbacks argument, in which case the
                EarlyStopping callback you specified will determine early
                stopping).
            callbacks: List of Keras callbacks to apply during training and
                validation.
            validation_split: Float between 0 and 1. Defaults to 0.2. Fraction
                of the training data to be used as validation data. The model
                will set apart this fraction of the training data, will not
                train on it, and will evaluate the loss and any model metrics on
                this data at the end of each epoch. The validation data is
                selected from the last samples in the `x` and `y` data provided,
                before shuffling. This argument is not supported when `x` is a
                dataset. The best model found would be fit on the entire
                dataset including the validation data.
            validation_data: Data on which to evaluate the loss and any model
                metrics at the end of each epoch. The model will not be trained
                on this data. `validation_data` will override
                `validation_split`. The type of the validation data should be
                the same as the training data.  The best model found would be
                fit on the training dataset without the validation data.
            **kwargs: Any arguments supported by
                [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).

        # Returns
            history: A Keras History object corresponding to the best model.
                Its History.history attribute is a record of training
                loss values and metrics values at successive epochs, as well as
                validation loss values and validation metrics values (if
                applicable).
        """
        history = super().fit(
            x=x,
            y=y,
            epochs=epochs,
            callbacks=callbacks,
            validation_split=validation_split,
            validation_data=validation_data,
            **kwargs
        )
        return history
Search for the best model and hyperparameters for the AutoModel.

        It will search for the best model based on the performances on
        validation data.

        # Arguments
            x: numpy.ndarray or tensorflow.Dataset. Training data x. The input
                data should be numpy.ndarray or tf.data.Dataset. The data should
                be one dimensional. Each element in the data should be a string
                which is a full sentence.
            y: numpy.ndarray or tensorflow.Dataset. Training data y. It can be
                raw labels, one-hot encoded if more than two classes, or binary
                encoded for binary classification.
            epochs: Int. The number of epochs to train each model during the
                search. If unspecified, by default we train for a maximum of
                1000 epochs, but we stop training if the validation loss stops
                improving for 10 epochs (unless you specified an EarlyStopping
                callback as part of the callbacks argument, in which case the
                EarlyStopping callback you specified will determine early
                stopping).
            callbacks: List of Keras callbacks to apply during training and
                validation.
            validation_split: Float between 0 and 1. Defaults to 0.2. Fraction
                of the training data to be used as validation data. The model
                will set apart this fraction of the training data, will not
                train on it, and will evaluate the loss and any model metrics on
                this data at the end of each epoch. The validation data is
                selected from the last samples in the `x` and `y` data provided,
                before shuffling. This argument is not supported when `x` is a
                dataset. The best model found would be fit on the entire
                dataset including the validation data.
            validation_data: Data on which to evaluate the loss and any model
                metrics at the end of each epoch. The model will not be trained
                on this data. `validation_data` will override
                `validation_split`. The type of the validation data should be
                the same as the training data.  The best model found would be
                fit on the training dataset without the validation data.
            **kwargs: Any arguments supported by
                [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit).

        # Returns
            history: A Keras History object corresponding to the best model.
                Its History.history attribute is a record of training
                loss values and metrics values at successive epochs, as well as
                validation loss values and validation metrics values (if
                applicable).
        
autokeras/tasks/text.py