code: def load_data_lm():
      dataset_file = cached_path(
          "https://s3.amazonaws.com/datasets.huggingface.co/wikitext-103/"
          "wikitext-103-train-tokenized-bert.bin"
      )
      datasets = torch.load(dataset_file)
  
      # Convert our encoded dataset to torch.tensors and reshape in blocks of the transformer's input length
      for split_name in ["train", "valid"]:
          tensor = torch.tensor(datasets[split_name], dtype=torch.long)
          num_sequences = (tensor.size(0) // 256) * 256
          datasets[split_name] = tensor.narrow(
              0, 0, num_sequences
          ).view(-1, 256)
  
      n = len(datasets["valid"]) // 2
      datasets["test"] = datasets["valid"][n:]
      datasets["valid"] = datasets["valid"][:n]
      datasets["train"] = datasets["train"][:1000]
      return datasets
  
file_path: transfer-nlp/experiments/transfer_learning/transformers/dataset.py
imports: import random
  import numpy
  import pandas
  import torch
  from pytorch_pretrained_bert import cached_path, BertTokenizer
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.plugins.config import register_plugin
label_desc: Use image node_id 1 for calling a global function with example usage: load_data_lm() and returns: datasets