code: def get_common_block(
      in_channels,
      dropout=0.25,
      use_bn=True,
      bn_track=True,
      use_spectral_norm=False,
  ):
      fc1 = nn.Linear(in_channels, in_channels)
      fc2 = nn.Linear(in_channels, in_channels)
      relu1 = nn.ReLU(inplace=True)
      relu2 = nn.ReLU(inplace=True)
      dp1 = nn.Dropout(dropout)
      dp2 = nn.Dropout(dropout)
      if use_spectral_norm:
          fc1 = spectral_norm(fc1)
          fc2 = spectral_norm(fc2)
      if use_bn:
          bn1 = nn.BatchNorm1d(
              in_channels, track_running_stats=bn_track
          )
          bn2 = nn.BatchNorm1d(
              in_channels, track_running_stats=bn_track
          )
          layers = [fc1, bn1, relu1, dp1, fc2, bn2, relu2, dp2]
      else:
          layers = [fc1, relu1, dp1, fc2, relu2, dp2]
      return nn.Sequential(*layers)
  
file_path: insightface/body/human_pose/ambiguity_aware/lib/models/building_blocks.py
imports: import math
  import torch
  import torch.nn
  from torch.nn.utils import spectral_norm
label_desc: Use image node_id 1 for calling a global function with example usage: get_common_block(in_channels, dropout, use_bn, bn_track, use_spectral_norm) and returns: nn