code: def __init__(self, file_name, split="train", seed=42):
      df = pd.read_csv(file_name)
      x = df.iloc[1:, 1:].values
      y = df.iloc[1:, 0].values
  
      # feature scaling
      sc = StandardScaler()
      x = sc.fit_transform(x)
      x = torch.tensor(x, dtype=torch.float32)
      y = torch.tensor(y, dtype=torch.long).unsqueeze(-1)
  
      # split
      self.split = split
      train_idx, test_idx = train_test_split(
          np.arange(len(x)), test_size=0.2, random_state=seed
      )
  
      if self.split == "train":
          self.x = x[train_idx]
          self.y = y[train_idx]
      elif self.split == "test":
          self.x = x[test_idx]
          self.y = y[test_idx]
  
file_path: baal/experiments/mlp_regression_mcdropout.py
imports: from copy import deepcopy
  from pprint import pprint
  import pandas
  import numpy
  import torch.cuda
  from torch import nn, optim
  from torch.utils.data import Dataset
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from baal import ActiveLearningDataset, ModelWrapper
  from baal.active import ActiveLearningLoop
  from baal.active.heuristics import Variance
  from baal.bayesian.dropout import patch_module
label_desc: Use image node_id 1 to create a new FeatureDataset object from inherited base classes: Dataset with example: obj = FeatureDataset(file_name, split, seed)