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 _setup_engine(self) -> None:
      if self.state.dataloader is None:
          raise ValueError(
              "Deterministic engine does not support the option of data=None. Please, provide data as iterable"
          )
  
      self._dataloader_len = self._get_data_length(
          self.state.dataloader
      )
  
      # if input data is torch dataloader we replace batch sampler by a batch sampler
      # such that its random sampling indices are reproducible by prefetching them before data iteration
      if isinstance(self.state.dataloader, DataLoader):
          # attribute _dataset_kind is introduced since 1.3.0 => before 1.3.0 all datasets are map-like
          can_patch_dataloader = True
          if hasattr(self.state.dataloader, "_dataset_kind"):
              from torch.utils.data.dataloader import _DatasetKind
  
              _dataloader_kind = self.state.dataloader._dataset_kind
              can_patch_dataloader = (
                  _dataloader_kind == _DatasetKind.Map
              )
          if can_patch_dataloader:
              if self._dataloader_len is not None and hasattr(
                  self.state.dataloader.sampler, "epoch"
              ):
                  if self._dataloader_len != self.state.epoch_length:
                      warnings.warn(
                          "When defined engine's epoch length is different of input dataloader length, "
                          "distributed sampler indices can not be setup in a reproducible manner"
                      )
  
              batch_sampler = self.state.dataloader.batch_sampler
              if not (
                  batch_sampler is None
                  or isinstance(batch_sampler, ReproducibleBatchSampler)
              ):
                  self.state.dataloader = update_dataloader(
                      self.state.dataloader, ReproducibleBatchSampler(batch_sampler)  # type: ignore[arg-type]
                  )
  
      iteration = self.state.iteration
      self._dataloader_iter = self._from_iteration(iteration)
  
      # Below we define initial counter value for _run_once_on_dataset to measure a single epoch
      if self.state.epoch_length is not None:
          iteration %= self.state.epoch_length
      self._init_iter = iteration
  
      # restore rng state if in the middle
      in_the_middle = (
          self.state.iteration % self._dataloader_len > 0
          if self._dataloader_len is not None
          else False
      )
      rng_states = getattr(self.state, "rng_states", None)
      if rng_states is not None and in_the_middle:
          _set_rng_states(rng_states)
          setattr(self.state, "rng_states", None)
  
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 4 for calling the DeterministicEngine obj's underlying member method code with example usage: obj._setup_engine() without return types