code: def get_accelerate_model(accelerator):
      global PORT
      PORT += 1
  
      is_master = accelerator.is_main_process
      world_size = accelerator.num_processes
      rank = accelerator.local_process_index
      store = TCPStore(
          "127.0.0.1",
          port=PORT,
          world_size=world_size,
          is_master=is_master,
      )
      dist_history = DistributedHistory(
          store=store, rank=rank, world_size=world_size
      )
  
      return AcceleratedNeuralNetClassifier(
          MyModule,
          criterion=nn.CrossEntropyLoss,
          accelerator=accelerator,
          max_epochs=3,
          lr=0.001,
          history=dist_history,
      )
  
imports: import numpy
  import torch
  from accelerate import Accelerator
  from sklearn.datasets import make_classification
  from sklearn.metrics import accuracy_score
  from torch import nn
  from torch.distributed import TCPStore
  from skorch import NeuralNetClassifier
  from skorch.hf import AccelerateMixin
  from skorch.history import DistributedHistory
label_desc: Use image node_id 1 for calling a global function with example usage: get_accelerate_model(accelerator) and returns: AcceleratedNeuralNetClassifier