class_docstr: Main entry point to the Opacus API - use ``PrivacyEngine``  to enable differential
  privacy for your model training.
  
  ``PrivacyEngine`` object encapsulates current privacy state (privacy budget +
  method it's been calculated) and exposes ``make_private`` method to wrap your
  PyTorch training objects with their private counterparts.
  
  Example:
      >>> dataloader = demo_dataloader
      >>> model = MyCustomModel()
      >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05)
      >>> privacy_engine = PrivacyEngine()
      >>>
      >>> model, optimizer, dataloader = privacy_engine.make_private(
      ...    module=model,
      ...    optimizer=optimizer,
      ...    data_loader=dataloader,
      ...    noise_multiplier=1.0,
      ...    max_grad_norm=1.0,
      ... )
      >>> # continue training as normal
class_docstr_tok: ["Main","entry","point","to","the","Opacus","API","-","use","``","PrivacyEngine","``","to","enable","differential","privacy","for","your","model","training",".","``","PrivacyEngine","``","object","encapsulates","current","privacy","state","(","privacy","budget","+","method","it","'s","been","calculated",")","and","exposes","``","make_private","``","method","to","wrap","your","PyTorch","training","objects","with","their","private","counterparts",".","Example",":",">",">",">","dataloader","=","demo_dataloader",">",">",">","model","=","MyCustomModel","(",")",">",">",">","optimizer","=","torch.optim.SGD","(","model.parameters","(",")",",","lr=0.05",")",">",">",">","privacy_engine","=","PrivacyEngine","(",")",">",">",">",">",">",">","model",",","optimizer",",","dataloader","=","privacy_engine.make_private","(","...","module=model",",","...","optimizer=optimizer",",","...","data_loader=dataloader",",","...","noise_multiplier=1.0",",","...","max_grad_norm=1.0",",","...",")",">",">",">","#","continue","training","as","normal"]
code: def _prepare_optimizer(
      self,
      optimizer: optim.Optimizer,
      *,
      noise_multiplier: float,
      max_grad_norm: Union[float, List[float]],
      expected_batch_size: int,
      loss_reduction: str = "mean",
      distributed: bool = False,
      clipping: str = "flat",
      noise_generator=None,
      grad_sample_mode="hooks",
  ) -> DPOptimizer:
      if isinstance(optimizer, DPOptimizer):
          optimizer = optimizer.original_optimizer
  
      generator = None
      if self.secure_mode:
          generator = self.secure_rng
      elif noise_generator is not None:
          generator = noise_generator
  
      optim_class = get_optimizer_class(
          clipping=clipping,
          distributed=distributed,
          grad_sample_mode=grad_sample_mode,
      )
  
      return optim_class(
          optimizer=optimizer,
          noise_multiplier=noise_multiplier,
          max_grad_norm=max_grad_norm,
          expected_batch_size=expected_batch_size,
          loss_reduction=loss_reduction,
          generator=generator,
          secure_mode=self.secure_mode,
      )
  
imports: import os
  import warnings
  from itertools import chain
  from typing import IO, Any, BinaryIO, Dict, List, Optional, Tuple, Union
  import torch
  from opacus.accountants import create_accountant
  from opacus.accountants.utils import get_noise_multiplier
  from opacus.data_loader import DPDataLoader, switch_generator
  from opacus.distributed import DifferentiallyPrivateDistributedDataParallel
  from opacus.grad_sample import AbstractGradSampleModule, GradSampleModule, get_gsm_class, wrap_model
  from opacus.optimizers import DPOptimizer, get_optimizer_class
  from opacus.schedulers import _GradClipScheduler, _NoiseScheduler
  from opacus.validators.module_validator import ModuleValidator
  from torch import nn, optim
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the PrivacyEngine obj's underlying member method code with example usage: obj._prepare_optimizer(optimizer) and returns: optim_class