code: def compression_preparation(model, compression_technique_list, mpu):
      """
      Prepare the compression techniques of a model.
      Args:
          model (`torch.nn.Module`)
              The model to prepare the compression techniques of.
          compression_technique_list (`list`)
              The list of compression techniques to prepare the model to.
              list[]
      """
      # Here we first replace all module with our linear wrapper
      for module_name, module in model.named_modules():
          if is_module_compressible(module, mpu):
              module_replacement(model, module_name, mpu=mpu)
      for (
          module_name_lists,
          _,
          compression_technique,
      ) in compression_technique_list:
          for mnl in module_name_lists:
              for module_name in mnl:
                  module_replacement(
                      model, module_name, compression_technique
                  )
  
      return model
  
file_path: DeepSpeed/deepspeed/compression/helper.py
imports: import torch
  from .basic_layer import Embedding_Compress, LinearLayer_Compress, Conv2dLayer_Compress, BNLayer_Compress, ColumnParallelLinear_Compress, RowParallelLinear_Compress
  from .constants import *
  from deepspeed.utils import logger
label_desc: Use image node_id 5 for calling a global function with example usage: compression_preparation(model, compression_technique_list, mpu) and returns: model