code: def _transformer_configs(c: Configs):
      """
      ### ALiBi based 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
      # GPT uses GELU activation for position wise feedforward
      conf.ffn.activation = "GELU"
  
      # ALiBi doesn't use positional embeddings
      conf.src_embed = "no_pos"
      conf.tgt_embed = "no_pos"
  
      # Set all attention mechanisms to ALiBi
      conf.encoder_attn = "alibi_mha"
      conf.decoder_attn = "alibi_mha"
      conf.decoder_mem_attn = "alibi_mha"
  
      #
      return conf
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/alibi/experiment.py
imports: import torch
  from torch.utils.data import DataLoader
  from labml import experiment, tracker
  from labml.configs import option, calculate
  from labml_helpers.datasets.text import SequentialUnBatchedDataset
  from labml_nn.transformers.alibi import AlibiMultiHeadAttention
  from labml_nn.experiments.nlp_autoregression import transpose_batch
  from labml_nn.transformers import TransformerConfigs
  from labml_nn.transformers.gpt import Configs
label_desc: Use image node_id 3 for calling a global function with example usage: _transformer_configs(c) and returns: conf