code: def __init__(
      self, in_feats, out_feats, k, activation=F.relu, bias=True
  ):
      super().__init__(
          in_feats, out_feats, k, activation=activation, bias=bias
      )
  
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 1 to create a new MChebConv object from inherited base classes: ChebConv with example: obj = MChebConv(in_feats, out_feats, k, activation, bias)