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 model size
      conf.d_model = c.d_model
      # Replace the encoder layer with a gMLP layer
      conf.encoder_layer = c.gmlp
  
      return conf
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/gmlp/experiment.py
imports: from labml import experiment
  from labml.configs import option
  from labml_nn.transformers import TransformerConfigs
  from labml_nn.transformers.basic.autoregressive_experiment import Configs
  from labml_nn.transformers.gmlp import GMLPBlock
label_desc: Use image node_id 2 for calling a global function with example usage: _transformer_configs(c) and returns: conf