code: def generate(self, model_len=None, model_width=None):
          
          if model_len is None:
              model_len = Constant.MODEL_LEN
          if model_width is None:
              model_width = Constant.MODEL_WIDTH
          if isinstance(model_width, list) and not len(model_width) == model_len:
              raise ValueError("The length of 'model_width' does not match 'model_len'")
          elif isinstance(model_width, int):
              model_width = [model_width] * model_len
  
          graph = Graph(self.input_shape, False)
          output_node_id = 0
          n_nodes_prev_layer = self.input_shape[0]
          for width in model_width:
              output_node_id = graph.add_layer(
                  StubDense(n_nodes_prev_layer, width), output_node_id
              )
              output_node_id = graph.add_layer(
                  StubDropout1d(Constant.MLP_DROPOUT_RATE), output_node_id
              )
              output_node_id = graph.add_layer(StubReLU(), output_node_id)
              n_nodes_prev_layer = width
  
          graph.add_layer(StubDense(n_nodes_prev_layer, self.n_output_node), output_node_id)
          return graph
docs: Generates a Multi-Layer Perceptron.
          Args:
              model_len: An integer. Number of hidden layers.
              model_width: An integer or a list of integers of length `model_len`. If it is a list, it represents the
                  number of nodes in each hidden layer. If it is an integer, all hidden layers have nodes equal to this
                  value.
          Returns:
              An instance of the class Graph. Represents the neural architecture graph of the generated model.