code: def __init__(self, low, high, num_features) -> None:
      self.samples = (
          torch.arange(start=low, end=high, dtype=torch.float)
          .repeat(num_features, 1)
          .transpose(1, 0)
      )
  
imports: import glob
  import tempfile
  from datetime import datetime
  from typing import cast, List
  import torch
  from captum._utils.av import AV
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicLinearReLULinear
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 1 to create a new RangeDataset object from inherited base classes: Dataset with example: obj = RangeDataset(low, high, num_features)