code: def forward(self, graph, feat):
      lambda_max = dgl.laplacian_lambda_max(graph)  # may be very slow
      return super().forward(graph, feat, lambda_max=lambda_max)
  
imports: import torch
  from torch import nn
  import torch.nn.functional
  import dgl
  from dgl.nn.pytorch.conv import *
  import dgl.function
  import math
  from dgl.utils import expand_as_pair
  from dgl.nn.functional import edge_softmax
  from torch.nn import Parameter
  import torch
  from torch import Tensor
  from ..scatter_utils import *
  from typing import Optional, Tuple, Union
label_desc: Use image node_id 2 for calling the MChebConv obj's underlying member method code with example usage: obj.forward(graph, feat) and returns: super