class_docstr: ScaledActivation module wraps an existing activation function and applies a
  scale factor to its output.
  
  Args:
      module (nn.Module): The activation function to be scaled.
      scales (torch.Tensor): A tensor of size (num_features,) containing the initial
          scale factors for each feature.
  
  Returns:
      torch.Tensor: The scaled output of the activation function.
class_docstr_tok: ["ScaledActivation","module","wraps","an","existing","activation","function","and","applies","a","scale","factor","to","its","output",".","Args",":","module","(","nn.Module",")",":","The","activation","function","to","be","scaled",".","scales","(","torch.Tensor",")",":","A","tensor","of","size","(","num_features",",",")","containing","the","initial","scale","factors","for","each","feature",".","Returns",":","torch.Tensor",":","The","scaled","output","of","the","activation","function","."]
code: def __init__(self, module, scales):
      super().__init__()
      self.act = module
      self.scales = nn.Parameter(scales.data)
  
file_path: llama.cpp/awq-py/awq/apply_awq.py
imports: import os
  import torch
  import torch.nn
  from transformers import AutoModelForCausalLM, AutoConfig
  from transformers.models.bloom.modeling_bloom import BloomGelu
  from transformers.models.llama.modeling_llama import LlamaRMSNorm
  from transformers.activations import GELUActivation
label_desc: Use image node_id 1 to create a new ScaledActivation object from inherited base classes: nn with example: obj = ScaledActivation(module, scales)