class_docstr: PyTorch specific Policy class to use with RLlib.
class_docstr_tok: ["PyTorch","specific","Policy","class","to","use","with","RLlib","."]
code: def compute_gradients(
      self, postprocessed_batch: SampleBatch
  ) -> ModelGradients:
      assert len(self.devices) == 1
  
      # If not done yet, see whether we have to zero-pad this batch.
      if not postprocessed_batch.zero_padded:
          pad_batch_to_sequences_of_same_size(
              batch=postprocessed_batch,
              max_seq_len=self.max_seq_len,
              shuffle=False,
              batch_divisibility_req=self.batch_divisibility_req,
              view_requirements=self.view_requirements,
          )
  
      postprocessed_batch.set_training(True)
      self._lazy_tensor_dict(
          postprocessed_batch, device=self.devices[0]
      )
  
      # Do the (maybe parallelized) gradient calculation step.
      tower_outputs = self._multi_gpu_parallel_grad_calc(
          [postprocessed_batch]
      )
  
      all_grads, grad_info = tower_outputs[0]
  
      grad_info["allreduce_latency"] /= len(self._optimizers)
      grad_info.update(self.extra_grad_info(postprocessed_batch))
  
      fetches = self.extra_compute_grad_fetches()
  
      return all_grads, dict(fetches, **{LEARNER_STATS_KEY: grad_info})
  
imports: import copy
  import functools
  import logging
  import math
  import os
  import threading
  import time
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
  import gymnasium
  import numpy
  import tree
  import ray
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.policy import Policy, PolicyState
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils import NullContextManager, force_list
  from ray.rllib.utils.annotations import DeveloperAPI, override
  from ray.rllib.utils.error import ERR_MSG_TORCH_POLICY_CANNOT_SAVE_MODEL
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics import DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY, NUM_AGENT_STEPS_TRAINED, NUM_GRAD_UPDATES_LIFETIME
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.space_utils import normalize_action
  from ray.rllib.utils.threading import with_lock
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
  from ray.rllib.utils.typing import AlgorithmConfigDict, GradInfoDict, ModelGradients, ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 9 for calling the TorchPolicy obj's underlying member method code with example usage: obj.compute_gradients(postprocessed_batch) and returns: all_grads,  dict