class_docstr: Masked language model for BERT style pre-training.
class_docstr_tok: ["Masked","language","model","for","BERT","style","pre-training","."]
code: def _mask_output(self, tokens, mask):
      return torch.masked_select(tokens, mask.bool())
  
imports: from typing import Dict, List
  import torch
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from pytext.common.constants import SpecialTokens, Stage
  from pytext.config import ConfigBase
  from pytext.data.bert_tensorizer import BERTTensorizerBase
  from pytext.data.tensorizers import Tensorizer
  from pytext.data.utils import Vocabulary
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.masking_utils import frequency_based_masking, MaskingStrategy, random_masking
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module
  from pytext.models.output_layers.lm_output_layer import LMOutputLayer
  from pytext.models.representations.transformer_sentence_encoder import TransformerSentenceEncoder
  from pytext.models.representations.transformer_sentence_encoder_base import TransformerSentenceEncoderBase
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 9 for calling the MaskedLanguageModel obj's underlying member method code with example usage: obj._mask_output(tokens, mask) and returns: torch