code: def forward(self, x):
      weight = self.weight
      weight_mean = (
          weight.mean(dim=1, keepdim=True)
          .mean(dim=2, keepdim=True)
          .mean(dim=3, keepdim=True)
      )
      weight = weight - weight_mean
      std = (
          weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1)
          + 1e-5
      )
      weight = weight / std.expand_as(weight)
      return F.conv2d(
          x,
          weight,
          self.bias,
          self.stride,
          self.padding,
          self.dilation,
          self.groups,
      )
  
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 2 for calling the StandardizedConv2d obj's underlying member method code with example usage: obj.forward(x) and returns: F