code: def _transformer_configs(c: Configs):
      """
      ### Transformer configurations
      """
  
      # We use our
      # [configurable transformer implementation](../configs.html#TransformerConfigs)
      conf = TransformerConfigs()
      # Set the vocabulary sizes for embeddings and generating logits
      conf.n_src_vocab = c.n_tokens
      conf.n_tgt_vocab = c.n_tokens
      # Embedding size
      conf.d_model = c.d_model
  
      #
      return conf
  
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 a global function with example usage: _transformer_configs(c) and returns: conf