class_docstr: Tensorflow backend datasets. 
class_docstr_tok: ["Tensorflow","backend","datasets","."]
code: def __getitem__(self, idx) -> Dict[str, np.ndarray]:
      if hasattr(self, "num_classes"):
          # Generate random targets for classification task.
          tgts = np.random.randint(
              low=0, high=self.num_classes, size=self.batch_size
          )
          Y_t = np.zeros((self.batch_size, self.num_classes))
          Y_t[np.arange(self.batch_size), tgts] = 1
      else:
          # Generate random target for regression task
          Y_t = np.random.uniform(
              low=self.min_m, high=self.max_m, size=(self.batch_size, 1)
          )
  
      # Select indices to be returned.
      indexes = self.indexes[
          idx * self.batch_size : (idx + 1) * self.batch_size
      ]
  
      # Compute conditional vector.
      C = self.conditional_func(self.X[indexes])
  
      return {
          "X": self.X[indexes],
          "Y_m": self.Y_m[indexes],
          "Y_t": Y_t,
          "C": C,
      }
  
imports: import os
  import random
  from typing import Any, List, Dict, Callable, Union, Optional, TYPE_CHECKING
  import numpy
  import tensorflow
  import tensorflow.keras
  from alibi.explainers.backends.cfrl_base import CounterfactualRLDataset
  from alibi.models.tensorflow.actor_critic import Actor, Critic
label_desc: Use image node_id 4 for calling the TfCounterfactualRLDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict