code: def __init__(self, input_channels: int) -> None:
      super().__init__()
  
      m = int(input_channels / 4)
      head_size = [[2], [1], [2]]
      heads = []
      for output_channels in sum(head_size, []):
          heads.append(
              nn.Sequential(
                  nn.Conv2d(
                      input_channels, m, kernel_size=3, padding=1
                  ),
                  nn.ReLU(inplace=True),
                  nn.Conv2d(m, output_channels, kernel_size=1),
              )
          )
      self.heads = nn.ModuleList(heads)
  
imports: from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Type, Union
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.functional import pixel_shuffle, softmax
  from kornia.core import Module, Tensor
label_desc: Use image node_id 1 to create a new MultitaskHead object from inherited base classes: Module with example: obj = MultitaskHead(input_channels)