Transpile the predict method.

        Parameters
        ----------
        :param temp_type : string
            The kind of export type (embedded, separated, exported).

        Returns
        -------
        :return : string
            The transpiled predict method as string.

def predict(self, temp_type):
        """
        Transpile the predict method.

        Parameters
        ----------
        :param temp_type : string
            The kind of export type (embedded, separated, exported).

        Returns
        -------
        :return : string
            The transpiled predict method as string.
        """
        # Exported:
        if temp_type == 'exported':
            temp = self.temp('exported.class')
            return temp.format(class_name=self.class_name,
                               method_name=self.method_name)
        # Separated:
        temp_arr = self.temp('arr')
        temp_arr_ = self.temp('arr[]')
        temp_arr__ = self.temp('arr[][]')
        temp_arr___ = self.temp('arr[][][]')

        # Activations:
        layers = list(self._get_activations())
        layers = ', '.join(layers)
        layers = temp_arr_.format(type='int', name='layers', values=layers)

        # Coefficients (weights):
        coefficients = []
        for layer in self.coefficients:
            layer_weights = []
            for weights in layer:
                weights = ', '.join([self.repr(w) for w in weights])
                layer_weights.append(temp_arr.format(weights))
            layer_weights = ', '.join(layer_weights)
            coefficients.append(temp_arr.format(layer_weights))
        coefficients = ', '.join(coefficients)
        coefficients = temp_arr___.format(type='double',
                                          name='weights',
                                          values=coefficients)

        # Intercepts (biases):
        intercepts = list(self._get_intercepts())
        intercepts = ', '.join(intercepts)
        intercepts = temp_arr__.format(type='double',
                                       name='bias',
                                       values=intercepts)

        temp_class = self.temp('separated.class')
        file_name = '{}.js'.format(self.class_name.lower())
        return temp_class.format(class_name=self.class_name,
                                 method_name=self.method_name,
                                 hidden_activation=self.hidden_activation,
                                 output_activation=self.output_activation,
                                 n_features=self.n_inputs,
                                 weights=coefficients,
                                 bias=intercepts,
                                 layers=layers,
                                 file_name=file_name)