class_docstr: Parallelformers end-point function
  
  Args:
      model (nn.Module): Huggingface pre-trained transformer model.
      fp16: (bool): whether use FP16 or not.
      num_gpus (int): number of GPU for parallelization.
      master_addr (str): master process address for process communication (default='127.0.0.1')
      master_port (int): master process port for process communication (default=29500)
      backend (str): distributed backend (default='nccl')
      verbose (str): logging current gpu states (one of ['detail', 'simple', None]
      init_method (str): multiprocess initialization method. (It is safe to set `init_method` to `spawn`.)
      daemon (bool): whether make process daemon or not (default=True)
  
  Notes:
      We want to use this object as a simple function rather than a class.
      So we broke the PEP8 rules and set class names that start with a lowercase letter.
  
  Examples:
      >>> # 1. Import Huggingface and Parallelformers modules
      >>> from transformers import AutoModelForCausalLM, AutoTokenizer
      >>> from parallelformers import parallelize
  
      >>> # 2. Create Huggingface model and tokenizer.
      >>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
      >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
  
      >>> # 3. Parallelize using Parallelformers.
      >>> parallelize(model, num_gpus=4, fp16=True)
  
      >>> # 4. Do inference as usual.
      >>> inputs = tokenizer("Parallelformers is", return_tensors="pt")
      >>> outputs = model.generate(**inputs, num_beams=5, no_repeat_ngram_size=4)
      >>> print(f"Output: {tokenizer.batch_decode(outputs)[0]}")
      'Output: Parallelformers is an open-source library for parallel programming ...'
docstr: There is one missing parameter in the Huggingface Wav2Vec model,
  and the user loses control over this parameter, making parallelization impossible.
  
  To use multiprocessing, if `requries_grad` is `True`, it must be a leaf tensor,
  but this tensor (`conv.weight`) does not follow this rule, so multiprocessing becomes impossible.
  Therefore, we detach this parameter to enable multiprocessing.
  
  Args:
      model (nn.Module): model weight
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code: def preprocess_for_wav2vec(self, model: nn.Module) -> None:
      """
      There is one missing parameter in the Huggingface Wav2Vec model,
      and the user loses control over this parameter, making parallelization impossible.
  
      To use multiprocessing, if `requries_grad` is `True`, it must be a leaf tensor,
      but this tensor (`conv.weight`) does not follow this rule, so multiprocessing becomes impossible.
      Therefore, we detach this parameter to enable multiprocessing.
  
      Args:
          model (nn.Module): model weight
      """
  
      with suppress(Exception):
          from transformers.models.hubert.modeling_hubert import (
              HubertPositionalConvEmbedding,
          )
          from transformers.models.wav2vec2.modeling_wav2vec2 import (
              Wav2Vec2PositionalConvEmbedding,
          )
  
          for child in model.named_children():
              layer_object = child[1]
  
              if layer_object.__class__ in [
                  Wav2Vec2PositionalConvEmbedding,
                  HubertPositionalConvEmbedding,
              ]:
                  detached_parameter = rgetattr(
                      layer_object, "conv.weight"
                  ).detach()
                  rsetattr(
                      layer_object, "conv.weight", detached_parameter
                  )
  
              else:
                  self.preprocess_for_wav2vec(layer_object)
  
imports: import os
  import traceback
  from contextlib import suppress
  from dataclasses import _is_dataclass_instance
  from typing import Any, Dict
  import torch
  import torch.multiprocessing
  from dacite import Config, from_dict
  from torch import nn
  from transformers.file_utils import ModelOutput
  from parallelformers.parallel.process import ParallelProcess
  from parallelformers.utils import rgetattr, rsetattr
label_desc: Use image node_id 2 for calling the parallelize obj's underlying member method code with example usage: obj.preprocess_for_wav2vec(model) without return types