class_docstr: Pytorch backend datasets. 
class_docstr_tok: ["Pytorch","backend","datasets","."]
code: def __getitem__(self, idx) -> Dict[str, np.ndarray]:
      if hasattr(self, "num_classes"):
          # Generate random target for classification task
          tgt = np.random.randint(low=0, high=self.num_classes, size=1)
          Y_t = np.zeros(self.num_classes)
          Y_t[tgt] = 1
      else:
          # Generate random target for regression task.
          Y_t = np.random.uniform(
              low=self.min_m, high=self.max_m, size=(1, 1)
          )
  
      data = {
          "X": self.X[idx],
          "Y_m": self.Y_m[idx],
          "Y_t": Y_t,
      }
  
      # Construct conditional vector.
      C = self.conditional_func(self.X[idx : idx + 1])
      if C is not None:
          data.update({"C": C.reshape(-1)})
  
      return data
  
imports: import os
  import random
  from typing import List, Dict, Callable, Union, Optional, TYPE_CHECKING
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, Dataset
  from alibi.explainers.backends.cfrl_base import CounterfactualRLDataset
  from alibi.models.pytorch.actor_critic import Actor, Critic
label_desc: Use image node_id 3 for calling the PtCounterfactualRLDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: data