class_docstr: From RobertaLMHead
class_docstr_tok: ["From","RobertaLMHead"]
code: def __init__(self, hidden_size, vocab_size, layer_norm_eps=1e-12):
      super().__init__()
      self.dense = nn.Linear(hidden_size, hidden_size)
      self.layer_norm = (
          transformers.models.bert.modeling_bert.BertLayerNorm(
              hidden_size, eps=layer_norm_eps
          )
      )
  
      self.decoder = nn.Linear(hidden_size, vocab_size, bias=False)
      self.bias = nn.Parameter(
          torch.zeros(vocab_size), requires_grad=True
      )
  
      # Need a link between the two variables so that the bias is correctly resized with
      # `resize_token_embeddings`
      self.decoder.bias = self.bias
  
imports: from __future__ import annotations
  import abc
  import torch
  import torch.nn
  import transformers
  from jiant.ext.allennlp import SelfAttentiveSpanExtractor
  from jiant.shared.model_resolution import ModelArchitectures
  from jiant.tasks.core import TaskTypes
  from typing import Callable
  from typing import List
label_desc: Use image node_id 1 to create a new RobertaMLMHead object from inherited base classes: BaseMLMHead with example: obj = RobertaMLMHead(hidden_size, vocab_size, layer_norm_eps)