code: def neftune_post_forward_hook(module, input, output):
      """
      Implements the NEFTune forward pass for the model using forward hooks. Note this works only for
      torch.nn.Embedding layers. This method is slightly adapted from the original source code
      that can be found here: https://github.com/neelsjain/NEFTune
  
      Simply add it to your model as follows:
      ```python
      model = ...
      model.embed_tokens.neftune_noise_alpha = 0.1
      model.embed_tokens.register_forward_hook(neftune_post_forward_hook)
      ```
  
      Args:
          module (`torch.nn.Module`):
              The embedding module where the hook is attached. Note that you need to set
              `module.neftune_noise_alpha` to the desired noise alpha value.
          input (`torch.Tensor`):
              The input tensor to the model.
          output (`torch.Tensor`):
              The output tensor of the model (i.e. the embeddings).
      """
      if module.training:
          dims = torch.tensor(output.size(1) * output.size(2))
          mag_norm = module.neftune_noise_alpha / torch.sqrt(dims)
          output = output + torch.zeros_like(output).uniform_(
              -mag_norm, mag_norm
          )
      return output
  
imports: import random
  import warnings
  from collections import deque
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import IterableDataset
  from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizerBase
  from ..import_utils import is_unsloth_available
label_desc: Use image node_id 6 for calling a global function with example usage: neftune_post_forward_hook(module, input, output) and returns: output