code: def export(self, class_name, method_name, **kwargs):
          
          # 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()
docs: 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.