class_docstr: GLUE base dataset class.
class_docstr_tok: ["GLUE","base","dataset","class","."]
code: def __init__(
      self,
      task_name,
      dataset_name,
      datapaths,
      tokenizer,
      max_seq_length,
  ):
      # Store inputs.
      self.task_name = task_name
      self.dataset_name = dataset_name
      self.tokenizer = tokenizer
      self.max_seq_length = max_seq_length
      print_rank_0(
          " > building {} dataset for {}:".format(
              self.task_name, self.dataset_name
          )
      )
      # Process the files.
      string = "  > paths:"
      for path in datapaths:
          string += " " + path
      print_rank_0(string)
      self.samples = []
      for datapath in datapaths:
          self.samples.extend(
              self.process_samples_from_single_path(datapath)
          )
      print_rank_0(
          "  >> total number of samples: {}".format(len(self.samples))
      )
  
file_path: megatron-lm/tasks/glue/data.py
imports: from abc import ABC
  from abc import abstractmethod
  from torch.utils.data import Dataset
  from megatron import print_rank_0
  from tasks.data_utils import build_sample
  from tasks.data_utils import build_tokens_types_paddings_from_text
label_desc: Use image node_id 1 to create a new GLUEAbstractDataset object from inherited base classes: ABC, Dataset with example: obj = GLUEAbstractDataset(task_name, dataset_name, datapaths, tokenizer, max_seq_length)