code: def initialize_pytorch(
      gpus: Optional[Union[int, str, List[int]]] = None,
      gpu_memory_limit: Optional[float] = None,
      allow_parallel_threads: bool = True,
      local_rank: int = 0,
      local_size: int = 1,
  ):
      param_tuple = (
          gpus,
          gpu_memory_limit,
          allow_parallel_threads,
          local_rank,
          local_size,
      )
      if _TORCH_INIT_PARAMS is not None:
          if _TORCH_INIT_PARAMS != param_tuple:
              warnings.warn(
                  "PyTorch has already been initialized. Changes to `gpus`, "
                  "`gpu_memory_limit`, and `allow_parallel_threads` will be ignored. "
                  "Start a new Python process to modify these values."
              )
          return
  
      # For reproducivility / determinism, set parallel threads to 1.
      # For performance, leave unset to allow PyTorch to select the best value automatically.
      if not allow_parallel_threads:
          torch.set_num_threads(1)
          torch.set_num_interop_threads(1)
          if torch.cuda.is_available():
              torch.backends.cudnn.deterministic = True
              torch.backends.cudnn.benchmark = False
  
      gpu_device_count = torch.cuda.device_count()
      if local_size > 1 and gpus is None:
          if 0 < gpu_device_count < local_size:
              warnings.warn(
                  f"Distributed: disabling GPU support! This host is running with "
                  f"{local_size} worker processes but only {gpu_device_count} "
                  f"GPUs. To enable GPU training, reduce the number of worker processes "
                  f"on this host to match the number of GPUs."
              )
              gpus = [-1]
          else:
              gpus = [local_rank]
  
      if isinstance(gpus, int):
          gpus = [gpus]
      elif isinstance(gpus, str):
          gpus = gpus.strip()
          gpus = [int(g) for g in gpus.split(",")]
  
      if gpus and len(gpus) == 1 and gpus[0] == -1:
          # CUDA_VISIBLE_DEVICES syntax for disabling all GPUs
          os.environ["CUDA_VISIBLE_DEVICES"] = ""
      elif torch.cuda.is_available():
          # Set visible devices so GPU utilization is isolated
          # (no GPU contention between workers).
          if gpus is not None:
              if len(gpus) == 1:
                  torch.cuda.set_device(gpus[0])
              elif len(gpus) > 1:
                  os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
                      str(i) for i in gpus
                  )
  
          # Limit the amount of memory that can be consumed per GPU
          if gpu_memory_limit is not None:
              for gpu in gpus or range(gpu_device_count):
                  torch.cuda.memory.set_per_process_memory_fraction(
                      gpu_memory_limit, gpu
                  )
  
      _set_torch_init_params(param_tuple)
  
imports: import math
  import os
  import warnings
  from abc import abstractmethod
  from functools import lru_cache
  from typing import List, Optional, Tuple, Union
  import torch
  from torch import nn
  from torch.nn import Module, ModuleDict
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import ENCODER_OUTPUT
  from ludwig.utils.strings_utils import SpecialSymbol
label_desc: Use image node_id 10 for calling a global function with example usage: initialize_pytorch(gpus, gpu_memory_limit, allow_parallel_threads, local_rank, local_size) without return types