class_docstr: Predict the RSSM's z^(t+1), given h(t), z^(t), and a(t).
  
  Disagreement (stddev) between the N networks in this model on what the next z^ would
  be are used to produce intrinsic rewards for enhanced, curiosity-based exploration.
  
  TODO
code: def forward_train(self, a, h, z):
      HxB = tf.shape(h)[0]
      # Fold z-dims.
      z = tf.reshape(z, shape=(HxB, -1))
      # Concat all input components (h, z, and a).
      inputs_ = tf.stop_gradient(tf.concat([h, z, a], axis=-1))
  
      z_predicted_probs_N_HxB = [
          repr(mlp(inputs_))[1]  # [0]=sample; [1]=returned probs
          for mlp, repr in zip(self.mlps, self.representation_layers)
      ]
      # shape=(N, HxB, [num categoricals], [num classes]); N=number of disagree nets.
      # HxB -> folded horizon_H x batch_size_B (from dreamed data).
  
      return {"z_predicted_probs_N_HxB": z_predicted_probs_N_HxB}
  
file_path: ray/rllib/algorithms/dreamerv3/tf/models/disagree_networks.py
imports: from ray.rllib.algorithms.dreamerv3.tf.models.components.mlp import MLP
  from ray.rllib.algorithms.dreamerv3.tf.models.components.representation_layer import RepresentationLayer
  from ray.rllib.utils.framework import try_import_tf, try_import_tfp
label_desc: Use image node_id 4 for calling the DisagreeNetworks obj's underlying member method code with example usage: obj.forward_train(a, h, z) and returns: dict