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.

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

def export(self, class_name, method_name, **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.

        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.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_

        if self.target_method == 'predict':
            return self.predict()