code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      bias=True,
  ):
      super(StandardizedConv2d, self).__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          dilation,
          groups,
          bias,
      )
  
imports: import logging
  import math
  import warnings
  from enum import Enum
  from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _ntuple
  from torch.utils.checkpoint import checkpoint
  from vissl.data.collators.collator_helper import MultiDimensionalTensor
  from vissl.utils.activation_checkpointing import checkpoint_trunk
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.misc import is_apex_available
label_desc: Use image node_id 1 to create a new StandardizedConv2d object from inherited base classes: nn with example: obj = StandardizedConv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)