code: def DistributedOptimizer(
      optimizer,
      named_parameters=None,
      compression=Compression.none,
      backward_passes_per_step=1,
      half=False,
      model=None,
      fp16_params=None,
      fp32_params=None,
      loss_scale=1024,
  ):
      """
      An optimizer that wraps another torch.optim.Optimizer, using an push_pull to
      average gradient values before applying gradients to model weights.
      push_pull operations are executed after each gradient is computed by `loss.backward()`
      in parallel with each other. The `step()` method ensures that all push_pull operations are
      finished before applying gradients to the model.
      DistributedOptimizer exposes the `synchronize()` method, which forces push_pull operations
      to finish before continuing the execution. It's useful in conjunction with gradient
      clipping, or other operations that modify gradients in place before `step()` is executed.
      Example of gradient clipping:
      ```
      output = model(data)
      loss = F.nll_loss(output, target)
      loss.backward()
      optimizer.synchronize()
      torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
      optimizer.step()
      ```
      Arguments:
          optimizer: Optimizer to use for computing gradients and applying updates.
          named_parameters: A mapping between parameter names and values. Used for naming of
                            push_pull operations. Typically just `model.named_parameters()`.
          compression: Compression algorithm used during push_pull to reduce the amount
                       of data sent during the each parameter update step.  Defaults to
                       not using compression.
          backward_passes_per_step: Number of expected backward passes to perform
                                    before calling step()/synchronize(). This
                                    allows accumulating gradients over multiple
                                    mini-batches before executing averaging and
                                    applying them.
      """
      # We dynamically create a new class that inherits from the optimizer that was passed in.
      # The goal is to override the `step()` method with an push_pull implementation.
      if half:
          cls = type(
              optimizer.__class__.__name__,
              (optimizer.__class__,),
              dict(_HalfPrecisionDistributedOptimizer.__dict__),
          )
          return cls(
              optimizer.param_groups,
              named_parameters,
              model,
              fp16_params,
              fp32_params,
              loss_scale,
              compression,
              backward_passes_per_step,
          )
      else:
          cls = type(
              optimizer.__class__.__name__,
              (optimizer.__class__,),
              dict(_DistributedOptimizer.__dict__),
          )
          return cls(
              optimizer.param_groups,
              named_parameters,
              compression,
              backward_passes_per_step,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from byteps.torch import _DistributedOptimizer
  from byteps.torch.compression import Compression
  from byteps.torch.ops import push_pull_async_inplace
  from byteps.torch.ops import push_pull
  from byteps.torch.ops import poll, synchronize, declare
  from byteps.torch.ops import init, shutdown
  from byteps.torch.ops import size, local_size, rank, local_rank
  import time
  import threading
  import torch
  import collections
label_desc: Use image node_id 1 for calling a global function with example usage: DistributedOptimizer(optimizer, named_parameters, compression, backward_passes_per_step, half, model, fp16_params, fp32_params, loss_scale) and returns: cls, cls