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
      # Set the embedding size
      conf.d_model = c.d_model
      # Replace self-attention with an [AFT Local Module](index.html)
      from labml_nn.transformers.aft import AFTLocal
  
      conf.encoder_attn = AFTLocal(
          c.d_model, c.seq_len, c.local_window_size
      )
  
      #
      return conf
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/aft/experiment.py
imports: import torch
  from labml import experiment
  from labml.configs import option
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_autoregression import NLPAutoRegressionConfigs
  from labml_nn.transformers import TransformerConfigs, Encoder
  from labml_nn.transformers.utils import subsequent_mask
label_desc: Use image node_id 1 for calling a global function with example usage: _transformer_configs(c) and returns: conf