class_docstr: Deterministic engine derived from :class:`~ignite.engine.engine.Engine`.
  
  "Deterministic" run is done by adding additional handlers to synchronize the dataflow and overriding some methods of
  :class:`~ignite.engine.engine.Engine`:
  
  .. code-block:: python
  
      for e in range(num_epochs):
          set_seed(seed_offset + e)
          if resume:
              setup_saved_rng_states()
          do_single_epoch_iterations(dataloader)
  
  If input data provider is `DataLoader`, its batch sampler is replaced by
  :class:`~ignite.engine.deterministic.ReproducibleBatchSampler`.
  
  .. code-block:: python
  
      for e in range(num_epochs):
          set_seed(seed_offset + e)
          setup_sampling(dataloader)
          if resume:
              setup_saved_rng_states()
          do_single_epoch_iterations(dataloader)
  
  Internally, `torch.backends.cudnn.deterministic = True` and `torch.backends.cudnn.benchmark = False` are also
  applied.
  
  For more details about dataflow synchronization, please see :ref:`Dataflow synchronization`.
  
  .. Note ::
  
      This class can produce exactly the same dataflow when resuming the run from an epoch (or more precisely from
      dataflow restart) and using torch `DataLoader` with `num_workers > 1` as data provider.
  
  Args:
      process_function: A function receiving a handle to the engine and the current batch
          in each iteration, and returns data to be stored in the engine's state.
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code: def _init_run(self) -> None:
      self.state.seed = int(torch.randint(0, int(1e9), (1,)).item())
      if torch.cuda.is_available():
          if hasattr(torch, "use_deterministic_algorithms"):
              torch.use_deterministic_algorithms(True, warn_only=True)
          else:
              torch.backends.cudnn.deterministic = True
          torch.backends.cudnn.benchmark = False
  
imports: import random
  import warnings
  from collections import OrderedDict
  from functools import wraps
  from typing import Any, Callable, Generator, Iterator, List, Optional
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data.sampler import BatchSampler
  from ignite.engine.engine import Engine
  from ignite.engine.events import Events
  from ignite.utils import manual_seed
label_desc: Use image node_id 3 for calling the DeterministicEngine obj's underlying member method code with example usage: obj._init_run() without return types