class_docstr: The dynamics (or "prior") network described in [1], producing prior z-states.
  
  The dynamics net is used to:
  - compute the initial z-state (from the tanh'd initial h-state variable) at the
  beginning of a sequence.
  - compute prior-z-states during dream data generation. Note that during dreaming,
  no actual observations are available and thus no posterior z-states can be computed.
docstr: Performs a forward pass through the dynamics (or "prior") network.
  
  Args:
      h: The deterministic hidden state of the sequence model.
  
  Returns:
      Tuple consisting of a differentiable z-sample and the probabilities for the
      categorical distribution (in the shape of [B, num_categoricals,
      num_classes]) that created this sample.
docstr_tok: ["Performs","a","forward","pass","through","the","dynamics","(","or","``","prior","''",")","network",".","Args",":","h",":","The","deterministic","hidden","state","of","the","sequence","model",".","Returns",":","Tuple","consisting","of","a","differentiable","z-sample","and","the","probabilities","for","the","categorical","distribution","(","in","the","shape","of","[","B",",","num_categoricals",",","num_classes","]",")","that","created","this","sample","."]
code: def call(self, h):
      """Performs a forward pass through the dynamics (or "prior") network.
  
      Args:
          h: The deterministic hidden state of the sequence model.
  
      Returns:
          Tuple consisting of a differentiable z-sample and the probabilities for the
          categorical distribution (in the shape of [B, num_categoricals,
          num_classes]) that created this sample.
      """
      # Send internal state through MLP.
      out = self.mlp(h)
      # Generate a z vector (stochastic, discrete sample).
      return self.representation_layer(out)
  
file_path: ray/rllib/algorithms/dreamerv3/tf/models/components/dynamics_predictor.py
imports: from typing import Optional
  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.algorithms.dreamerv3.utils import get_gru_units
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 2 for calling the DynamicsPredictor obj's underlying member method code with example usage: obj.call(h) and returns: self