class_docstr: PyOD Dataset class for PyTorch Dataloader
      
code: def __getitem__(self, idx):
      if torch.is_tensor(idx):
          idx = idx.tolist()
      sample = self.X[idx, :]
  
      if self.mean is not None and self.std is not None:
          sample = (sample - self.mean) / self.std
          # assert_almost_equal (0, sample.mean(), decimal=1)
  
      return torch.from_numpy(sample), idx
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import torch
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from torch import nn
  from .base import BaseDetector
  from ..utils.stat_models import pairwise_distances_no_broadcast
  from ..utils.torch_utility import get_activation_by_name
label_desc: Use image node_id 3 for calling the PyODDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: torch,  idx