class_docstr: RoBERTa encoder.
class_docstr_tok: ["RoBERTa","encoder","."]
code: def build_embedding(self, vocab_size, embedding_dim, padding_idx):
      return VocabParallelEmbedding(
          vocab_size, embedding_dim, padding_idx
      )
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.roberta import roberta_base_architecture, roberta_prenorm_architecture, RobertaEncoder, RobertaModel
  from fairseq.modules import LayerNorm
label_desc: Use image node_id 2 for calling the ModelParallelRobertaEncoder obj's underlying member method code with example usage: obj.build_embedding(vocab_size, embedding_dim, padding_idx) and returns: VocabParallelEmbedding