def evaluate(self, x, y=None, batch_size=32, verbose=1, **kwargs):
        """Evaluate the best model for the given data.

        # Arguments
            x: Any allowed types according to the input node. Testing data.
            y: Any allowed types according to the head. Testing targets.
                Defaults to None.
            batch_size: Number of samples per batch.
                If unspecified, batch_size will default to 32.
            verbose: Verbosity mode. 0 = silent, 1 = progress bar.
                Controls the verbosity of
                [keras.Model.evaluate](http://tensorflow.org/api_docs/python/tf/keras/Model#evaluate)
            **kwargs: Any arguments supported by keras.Model.evaluate.

        # Returns
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs and/or
            metrics). The attribute model.metrics_names will give you the
            display labels for the scalar outputs.
        """
        self._check_data_format((x, y))
        if isinstance(x, tf.data.Dataset):
            dataset = x
            x = dataset.map(lambda x, y: x)
            y = dataset.map(lambda x, y: y)
        x = self._adapt(x, self.inputs, batch_size)
        y = self._adapt(y, self._heads, batch_size)
        dataset = tf.data.Dataset.zip((x, y))
        pipeline = self.tuner.get_best_pipeline()
        dataset = pipeline.transform(dataset)
        model = self.tuner.get_best_model()
        return utils.evaluate_with_adaptive_batch_size(
            model=model,
            batch_size=batch_size,
            x=dataset,
            verbose=verbose,
            **kwargs
        )
Evaluate the best model for the given data.

        # Arguments
            x: Any allowed types according to the input node. Testing data.
            y: Any allowed types according to the head. Testing targets.
                Defaults to None.
            batch_size: Number of samples per batch.
                If unspecified, batch_size will default to 32.
            verbose: Verbosity mode. 0 = silent, 1 = progress bar.
                Controls the verbosity of
                [keras.Model.evaluate](http://tensorflow.org/api_docs/python/tf/keras/Model#evaluate)
            **kwargs: Any arguments supported by keras.Model.evaluate.

        # Returns
            Scalar test loss (if the model has a single output and no metrics)
            or list of scalars (if the model has multiple outputs and/or
            metrics). The attribute model.metrics_names will give you the
            display labels for the scalar outputs.
        
autokeras/auto_model.py