docstr: The forward pass for the MLM task
  
  Args:
      src_tokens (torch.Tensor):
          The masked token indices. Shape: (batch, seq_len)
      attention_mask (torch.Tensor):
          The attention mask, since the batches are padded
          to the largest sequence. Shape: (batch, seq_len)
      tgt_tokens (torch.Tensor):
          The output tokens (padded with `config.pad_token_id`)
  
  Returns:
      torch.Tensor:
          The MLM loss
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code: def forward(
      self,
      src_tokens: torch.Tensor,
      attention_mask: torch.Tensor,
      tgt_tokens: torch.Tensor,
  ) -> torch.Tensor:
      """The forward pass for the MLM task
  
      Args:
          src_tokens (torch.Tensor):
              The masked token indices. Shape: (batch, seq_len)
          attention_mask (torch.Tensor):
              The attention mask, since the batches are padded
              to the largest sequence. Shape: (batch, seq_len)
          tgt_tokens (torch.Tensor):
              The output tokens (padded with `config.pad_token_id`)
  
      Returns:
          torch.Tensor:
              The MLM loss
      """
      # shape: (batch, seq_len, h_dim)
      sequence_output, *_ = self.encoder(
          input_ids=src_tokens,
          attention_mask=attention_mask,
          return_dict=False,
      )
  
      pad_token_id = self.config.pad_token_id
      # (labels have also been padded with pad_token_id)
      # filter out all masked labels
      # shape: (num_masked_tokens,)
      masked_token_indexes = torch.nonzero(
          (tgt_tokens != pad_token_id).view(-1)
      ).view(-1)
      # shape: (num_masked_tokens, vocab_size)
      prediction_scores = self.lm_head(
          sequence_output, masked_token_indexes
      )
      # shape: (num_masked_tokens,)
      target = torch.index_select(
          tgt_tokens.view(-1), 0, masked_token_indexes
      )
  
      loss_fct = nn.CrossEntropyLoss(ignore_index=-1)
  
      masked_lm_loss = loss_fct(
          prediction_scores.view(-1, self.config.vocab_size), target
      )
      return masked_lm_loss
  
imports: import datetime
  import json
  import pathlib
  import re
  import string
  from functools import partial
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, TypeVar, Union
  import random
  import datasets
  import fire
  import loguru
  import numpy
  import pytz
  import sh
  import torch
  import torch.nn
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.tensorboard import SummaryWriter
  from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
  from transformers.models.roberta import RobertaConfig, RobertaModel
  from transformers.models.roberta.modeling_roberta import RobertaLMHead, RobertaPreTrainedModel
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling the RobertaMLMModel obj's underlying member method code with example usage: obj.forward(src_tokens, attention_mask, tgt_tokens) and returns: masked_lm_loss