def fit(
        self,
        x=None,
        y=None,
        batch_size=32,
        epochs=None,
        callbacks=None,
        validation_split=0.2,
        validation_data=None,
        verbose=1,
        **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.
            y: numpy.ndarray or tensorflow.Dataset. Training data y.
            batch_size: Int. Number of samples per gradient update. Defaults to
                32.
            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.
            verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar,
                2 = one line per epoch. Note that the progress bar is not
                particularly useful when logged to a file, so verbose=2 is
                recommended when not running interactively (eg, in a production
                environment). Controls the verbosity of both KerasTuner search
                and
                [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)
            **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).
        """
        # Check validation information.
        if not validation_data and not validation_split:
            raise ValueError(
                "Either validation_data or a non-zero validation_split "
                "should be provided."
            )

        if validation_data:
            validation_split = 0

        dataset, validation_data = self._convert_to_dataset(
            x=x, y=y, validation_data=validation_data, batch_size=batch_size
        )
        self._analyze_data(dataset)
        self._build_hyper_pipeline(dataset)

        # Split the data with validation_split.
        if validation_data is None and validation_split:
            dataset, validation_data = data_utils.split_dataset(
                dataset, validation_split
            )

        history = self.tuner.search(
            x=dataset,
            epochs=epochs,
            callbacks=callbacks,
            validation_data=validation_data,
            validation_split=validation_split,
            verbose=verbose,
            **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.
            y: numpy.ndarray or tensorflow.Dataset. Training data y.
            batch_size: Int. Number of samples per gradient update. Defaults to
                32.
            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.
            verbose: 0, 1, or 2. Verbosity mode. 0 = silent, 1 = progress bar,
                2 = one line per epoch. Note that the progress bar is not
                particularly useful when logged to a file, so verbose=2 is
                recommended when not running interactively (eg, in a production
                environment). Controls the verbosity of both KerasTuner search
                and
                [keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit)
            **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/auto_model.py