Port a trained estimator to the syntax of a chosen programming language.

        Parameters
        ----------
        :param class_name : string
            The name of the class in the returned result.
        :param method_name : string
            The name of the method in the returned result.
        :param export_data : bool, default: False
            Whether the model data should be saved or not.
        :param export_dir : string, default: '.' (current directory)
            The directory where the model data should be saved.
        :param export_filename : string, default: 'data.json'
            The filename of the exported model data.
        :param export_append_checksum : bool, default: False
            Whether to append the checksum to the filename or not.

        Returns
        -------
        :return : string
            The transpiled algorithm with the defined placeholders.

def export(self, class_name, method_name, export_data=False,
               export_dir='.', export_filename='data.json',
               export_append_checksum=False, **kwargs):
        """
        Port a trained estimator to the syntax of a chosen programming language.

        Parameters
        ----------
        :param class_name : string
            The name of the class in the returned result.
        :param method_name : string
            The name of the method in the returned result.
        :param export_data : bool, default: False
            Whether the model data should be saved or not.
        :param export_dir : string, default: '.' (current directory)
            The directory where the model data should be saved.
        :param export_filename : string, default: 'data.json'
            The filename of the exported model data.
        :param export_append_checksum : bool, default: False
            Whether to append the checksum to the filename or not.

        Returns
        -------
        :return : string
            The transpiled algorithm with the defined placeholders.
        """
        # Arguments:
        self.class_name = class_name
        self.method_name = method_name

        # Estimator:
        est = self.estimator

        self.output_activation = est.out_activation_
        self.hidden_activation = est.activation

        self.n_layers = est.n_layers_
        self.n_hidden_layers = est.n_layers_ - 2

        self.n_inputs = len(est.coefs_[0])
        self.n_outputs = est.n_outputs_

        self.hidden_layer_sizes = est.hidden_layer_sizes
        if isinstance(self.hidden_layer_sizes, int):
            self.hidden_layer_sizes = [self.hidden_layer_sizes]
        self.hidden_layer_sizes = list(self.hidden_layer_sizes)

        self.layer_units = \
            [self.n_inputs] + self.hidden_layer_sizes + [est.n_outputs_]

        # Weights:
        self.coefficients = est.coefs_

        # Bias:
        self.intercepts = est.intercepts_

        # Binary or multiclass classifier?
        self.is_binary = self.n_outputs == 1
        self.prefix = 'binary' if self.is_binary else 'multi'

        if self.target_method == 'predict':
            # Exported:
            if export_data and os.path.isdir(export_dir):
                self.export_data(export_dir, export_filename,
                                 export_append_checksum)
                return self.predict('exported')
            # Separated:
            return self.predict('separated')