code: def set_up_training(self, state: TrainingState, training_data):
      training_data = super().set_up_training(state, training_data)
  
      # Share memory of tensors for concurrent updates from multiple processes.
      if self.num_workers > 1:
          for param in state.model.parameters():
              param.share_memory_()
  
      return training_data
  
file_path: pytext/pytext/trainers/hogwild_trainer.py
imports: import copy
  import torch
  import torch.multiprocessing
  from pytext.common.constants import Stage
  from pytext.config.pytext_config import ConfigBase
  from pytext.legacy.data import Iterator
  from pytext.metric_reporters import MetricReporter
  from pytext.trainers.trainer import TaskTrainer, Trainer, TrainingState
  from pytext.utils import cuda
label_desc: Use image node_id 4 for calling the HogwildTrainer_Deprecated obj's underlying member method code with example usage: obj.set_up_training(state, training_data) and returns: training_data