code: def dataio_prepare(hparams):
      """This function prepares the datasets to be used in the brain class.
      It also defines the data processing pipeline through user-defined functions.
  
      The language model is trained with the text files specified by the user in
      the hyperparameter file.
  
      Arguments
      ---------
      hparams : dict
          This dictionary is loaded from the `train.yaml` file, and it includes
          all the hyperparameters needed for dataset construction and loading.
  
      Returns
      -------
      datasets : list
          List containing "train", "valid", and "test" sets that correspond
          to the appropriate DynamicItemDataset object.
      """
  
      logging.info("generating datasets...")
  
      # Prepare datasets
      datasets = load_dataset(
          "text",
          data_files={
              "train": hparams["lm_train_data"],
              "valid": hparams["lm_valid_data"],
              "test": hparams["lm_test_data"],
          },
      )
  
      # Convert huggingface's dataset to DynamicItemDataset via a magical function
      train_data = (
          sb.dataio.dataset.DynamicItemDataset.from_arrow_dataset(
              datasets["train"]
          )
      )
      valid_data = (
          sb.dataio.dataset.DynamicItemDataset.from_arrow_dataset(
              datasets["valid"]
          )
      )
      test_data = (
          sb.dataio.dataset.DynamicItemDataset.from_arrow_dataset(
              datasets["test"]
          )
      )
  
      datasets = [train_data, valid_data, test_data]
      tokenizer = hparams["tokenizer"]
  
      # Define text processing pipeline. We start from the raw text and then
      # encode it using the tokenizer. The tokens with bos are used for feeding
      # the neural network, the tokens with eos for computing the cost function.
      @sb.utils.data_pipeline.takes("text")
      @sb.utils.data_pipeline.provides(
          "text", "tokens_bos", "tokens_eos"
      )
      def text_pipeline(text):
          """Defines the pipeline that processes the input text."""
          yield text
          tokens_list = tokenizer.encode_as_ids(text)
          tokens_bos = torch.LongTensor(
              [hparams["bos_index"]] + (tokens_list)
          )
          yield tokens_bos
          tokens_eos = torch.LongTensor(
              tokens_list + [hparams["eos_index"]]
          )
          yield tokens_eos
  
      sb.dataio.dataset.add_dynamic_item(datasets, text_pipeline)
  
      # 4. Set outputs to add into the batch. The batch variable will contain
      # all these fields (e.g, batch.id, batch.text, batch.tokens.bos,..)
      sb.dataio.dataset.set_output_keys(
          datasets,
          ["id", "text", "tokens_bos", "tokens_eos"],
      )
      return train_data, valid_data, test_data
  
file_path: speechbrain/templates/speech_recognition/LM/train.py
imports: import sys
  import logging
  import torch
  from datasets import load_dataset
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 1 for calling a global function with example usage: dataio_prepare(hparams) and returns: train_data,  valid_data,  test_data