code: def __init__(self, cfg, feature_dim: int):
      super(Transformer, self).__init__()
  
      self.input_norm = LayerNormalization(feature_dim)
      self.input_fc = None
      input_dim = feature_dim
  
      if cfg.INPUT_FC:
          self.input_fc = nn.Sequential(
              nn.Linear(feature_dim, cfg.INPUT_FC_OUTPUT_DIM), nn.GELU()
          )
          input_dim = cfg.INPUT_FC_OUTPUT_DIM
      self.embedding = PositionalEncoding(
          input_dim, cfg.DROPOUT, max_len=1000
      )
  
      self.tf = TransformerEncoder(
          cfg.NUM_LAYERS,
          input_dim,
          cfg.NUM_HEADS,
          input_dim,
          cfg.DROPOUT,
      )
  
      self.use_context = cfg.USE_CONTEXT
      if self.use_context:
          self.tf_context = TransformerEncoder(
              cfg.ATN_CTX_NUM_LAYERS,
              input_dim,
              cfg.ATN_CTX_NUM_HEADS,
              input_dim,
              cfg.DROPOUT,
          )
  
      self.pooler = parse_pooler(input_dim, cfg)
  
      init_network(self, init_std=0.01)
  
imports: from collections import OrderedDict
  import os
  import numpy
  import torch
  from torch import nn
  from gluoncv.torch.utils.coot_utils import truncated_normal_fill
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(cfg, feature_dim)