class_docstr: # Transformer based model for MLM
class_docstr_tok: ["#","Transformer","based","model","for","MLM"]
code: def forward(self, x: torch.Tensor):
      # Get the token embeddings with positional encodings
      x = self.src_embed(x)
      # Transformer encoder
      x = self.encoder(x, None)
      # Logits for the output
      y = self.generator(x)
  
      # Return results
      # (second value is for state, since our trainer is used with RNNs also)
      return y, None
  
imports: from typing import List
  import torch
  from torch import nn
  from labml import experiment, tracker, logger
  from labml.configs import option
  from labml.logger import Text
  from labml_helpers.metrics.accuracy import Accuracy
  from labml_helpers.module import Module
  from labml_helpers.train_valid import BatchIndex
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers import Encoder, Generator
  from labml_nn.transformers import TransformerConfigs
  from labml_nn.transformers.mlm import MLM
label_desc: Use image node_id 2 for calling the TransformerMLM obj's underlying member method code with example usage: obj.forward(x) and returns: y,  None