class_docstr: PyTorch specific Policy class to use with RLlib.
class_docstr_tok: ["PyTorch","specific","Policy","class","to","use","with","RLlib","."]
docstr: Called after each optimizer.zero_grad() + loss.backward() call.
  
  Called for each self._optimizers/loss-value pair.
  Allows for gradient processing before optimizer.step() is called.
  E.g. for gradient clipping.
  
  Args:
      optimizer: A torch optimizer object.
      loss: The loss tensor associated with the optimizer.
  
  Returns:
      An dict with information on the gradient processing step.
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code: def extra_grad_process(
      self, optimizer: "torch.optim.Optimizer", loss: TensorType
  ) -> Dict[str, TensorType]:
      """Called after each optimizer.zero_grad() + loss.backward() call.
  
      Called for each self._optimizers/loss-value pair.
      Allows for gradient processing before optimizer.step() is called.
      E.g. for gradient clipping.
  
      Args:
          optimizer: A torch optimizer object.
          loss: The loss tensor associated with the optimizer.
  
      Returns:
          An dict with information on the gradient processing step.
      """
      return {}
  
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 19 for calling the TorchPolicy obj's underlying member method code with example usage: obj.extra_grad_process(optimizer, loss) and returns: dict