code: def __init__(self, args, encoder):
      super().__init__(encoder)
      self.args = args
  
      # We follow BERT's random weight initialization
      self.apply(init_bert_params)
  
      self.classification_heads = nn.ModuleDict()
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
  from fairseq.modules import LayerNorm
  from fairseq.modules.quant_noise import quant_noise
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_getattr, safe_hasattr
  from .hub_interface import RobertaHubInterface
label_desc: Use image node_id 2 to create a new RobertaModel object from inherited base classes: FairseqEncoderModel with example: obj = RobertaModel(args, encoder)