code: def __init__(self, train=True):
      data = load_diabetes()
      self.x = torch.tensor(data["data"], dtype=torch.float32)
      self.y = torch.tensor(data["target"], dtype=torch.float32)
      self.length = self.x.shape[0]
      split = int(self.length * 0.8)
      if train:
          self.x = self.x[:split]
          self.y = self.y[:split]
      else:
          self.x = self.x[split:]
          self.y = self.y[split:]
      self.length = len(self.y)
  
imports: import pytest
  import pytorch_lightning
  import torch
  import torch.nn
  import torch.nn.functional
  from sklearn.datasets import load_diabetes
  from torch.utils.data import Dataset
  from torchvision import transforms
  from torchvision.datasets import MNIST
  import nni
  import nni.nas.evaluator.pytorch.lightning
  from nni.nas.evaluator import FunctionalEvaluator, Evaluator
  from nni.nas.space import RawFormatModelSpace
label_desc: Use image node_id 1 to create a new DiabetesDataset object from inherited base classes: Dataset with example: obj = DiabetesDataset(train)