code: def __init__(
      self,
      vocab_size=30522,
      num_hidden_layers=4,
      num_attention_heads=4,
      position_embeddings_size=512,
      intermediate_size=3072,
      input_feat_dim=[2048, 512, 128],
      hidden_feat_dim=[1024, 256, 128],
      attention_probs_dropout_prob=0.1,
      fc_dropout_prob=0.1,
      act_fn="gelu",
      output_attentions=False,
      output_hidden_feats=False,
  ):
      super(TransEncoder, self).__init__()
      output_feat_dim = input_feat_dim[1:] + [3]
      trans_encoder = []
      for i in range(len(output_feat_dim)):
          features_dims = input_feat_dim[i]
          output_feature_dim = output_feat_dim[i]
          hidden_size = hidden_feat_dim[i]
  
          # init a transformer encoder and append it to a list
          assert hidden_size % num_attention_heads == 0
          model = METROEncoder(
              vocab_size,
              num_hidden_layers,
              features_dims,
              position_embeddings_size,
              hidden_size,
              intermediate_size,
              output_feature_dim,
              num_attention_heads,
              attention_probs_dropout_prob,
              fc_dropout_prob,
              act_fn,
              output_attentions,
              output_hidden_feats,
          )
          trans_encoder.append(model)
      self.trans_encoder = paddle.nn.Sequential(*trans_encoder)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import ReLU, Swish, GELU
  import math
  from ppdet.core.workspace import register
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 1 to create a new TransEncoder object from inherited base classes: nn with example: obj = TransEncoder(vocab_size, num_hidden_layers, num_attention_heads, position_embeddings_size, intermediate_size, input_feat_dim, hidden_feat_dim, attention_probs_dropout_prob, fc_dropout_prob, act_fn, output_attentions, output_hidden_feats)