code: def __init__(
      self,
      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="ReLU",
      output_attentions=False,
      output_hidden_feats=False,
      use_img_layernorm=False,
  ):
      super(METROEncoder, self).__init__()
      self.img_dims = features_dims
      self.num_hidden_layers = num_hidden_layers
      self.use_img_layernorm = use_img_layernorm
      self.output_attentions = output_attentions
      self.embedding = BertEmbeddings(
          vocab_size,
          position_embeddings_size,
          2,
          hidden_size,
          fc_dropout_prob,
      )
      self.encoder = BertEncoder(
          num_hidden_layers,
          hidden_size,
          intermediate_size,
          num_attention_heads,
          attention_probs_dropout_prob,
          fc_dropout_prob,
          act_fn,
          output_attentions,
          output_hidden_feats,
      )
      self.pooler = BertPooler(hidden_size)
      self.position_embeddings = nn.Embedding(
          position_embeddings_size, hidden_size
      )
      self.img_embedding = nn.Linear(
          features_dims, hidden_size, bias_attr=True
      )
      self.dropout = nn.Dropout(fc_dropout_prob)
      self.cls_head = nn.Linear(hidden_size, output_feature_dim)
      self.residual = nn.Linear(features_dims, output_feature_dim)
  
      self.apply(self.init_weights)
  
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 METROEncoder object from inherited base classes: nn with example: obj = 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, use_img_layernorm)