class_docstr: Typesafe method for indicating `apply_gradients` can directly step the
  optimizers with in-place gradients.
class_docstr_tok: ["Typesafe","method","for","indicating","`","apply_gradients","`","can","directly","step","the","optimizers","with","in-place","gradients","."]
code: def __repr__(self):
      return "DirectStepOptimizer"
  
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 3 for calling the DirectStepOptimizer obj's underlying member method code with example usage: obj.__repr__() and returns: str