class_docstr: Action distribution that operates on multiple, possibly nested actions.
class_docstr_tok: ["Action","distribution","that","operates","on","multiple",",","possibly","nested","actions","."]
code: def to_deterministic(self) -> "TorchMultiDistribution":
      flat_deterministic_dists = [
          dist.to_deterministic()
          for dist in self._flat_child_distributions
      ]
      deterministic_dists = tree.unflatten_as(
          self._original_struct, flat_deterministic_dists
      )
      return TorchMultiDistribution(deterministic_dists)
  
imports: import gymnasium
  import numpy
  from typing import Optional, List, Mapping, Iterable, Dict
  import tree
  import abc
  from ray.rllib.models.distributions import Distribution
  from ray.rllib.utils.annotations import override, DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import TensorType, Union, Tuple
label_desc: Use image node_id 9 for calling the TorchMultiDistribution obj's underlying member method code with example usage: obj.to_deterministic() and returns: TorchMultiDistribution