code: def generate_cf(
      Z: Union[np.ndarray, tf.Tensor],
      Y_m: Union[np.ndarray, tf.Tensor],
      Y_t: Union[np.ndarray, tf.Tensor],
      C: Optional[Union[np.ndarray, tf.Tensor]],
      actor: keras.Model,
      **kwargs,
  ) -> tf.Tensor:
      """
      Generates counterfactual embedding.
  
      Parameters
      ----------
      Z
          Input embedding tensor.
      Y_m
          Input classification label.
      Y_t
          Target counterfactual classification label.
      C
          Conditional tensor.
      actor
          Actor network. The model generates the counterfactual embedding.
      **kwargs
          Other arguments. Not used.
  
      Returns
      -------
      Z_cf
          Counterfactual embedding.
      """
      # Convert labels, targets and condition float32
      Y_m = tf.cast(Y_m, dtype=tf.float32)
      Y_t = tf.cast(Y_t, dtype=tf.float32)
      C = tf.cast(C, dtype=tf.float32) if (C is not None) else C
  
      # Concatenate z_mean, y_m_ohe, y_t_ohe to create the input representation for the projection network (actor).
      state = [Z, Y_m, Y_t] + ([C] if (C is not None) else [])
      state = tf.concat(state, axis=1)
  
      # Pass the new input to the projection network (actor) to get the counterfactual embedding
      Z_cf = actor(state, training=False)
      return Z_cf
  
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 9 for calling a global function with example usage: generate_cf(Z, Y_m, Y_t, C, actor) and returns: Z_cf