code: def init_compression(
      model, deepspeed_config, teacher_model=None, mpu=None
  ):
      """
      Compress a model: replace linear/conv2d layer with deepspeed compression-aware modules
      Args:
          model (`torch.nn.Module`)
              The model to compress.
          deepspeed_config (`DeepSpeedConfig`)
              The path of ds_config
          mpu
              The mpu module for Row/Column parallelism
      """
      compress_methods = get_compression_config(
          check_deepspeed_config(deepspeed_config)
      )
      if hasattr(model, "module"):
          c_model = model.module
      else:
          c_model = model
  
      # For layer reduction
      if compress_methods[LAYER_REDUCTION][LAYER_REDUCTION_ENABLED]:
          assert (
              teacher_model is not None
          ), "Teacher model is required for layer reduction"
          student_initialization(
              c_model, teacher_model, deepspeed_config
          )
  
      layer_added_compress_methods = get_compress_methods(
          c_model, compress_methods, mpu=mpu
      )
      compression_preparation(
          c_model, layer_added_compress_methods, mpu
      )
  
      # For sparse pruning snip_momentum method
      shared_parameters = compress_methods[SPARSE_PRUNING][
          SHARED_PARAMETERS
      ]
      if (
          shared_parameters[SPARSE_PRUNING_ENABLED]
          and shared_parameters[SPARSE_PRUNING_METHOD]
          == SPARSE_PRUNING_METHOD_SNIP_MOMENTUM
      ):
          assert (
              nc is not None
          ), "please ensure the neural_compressor python package is installed by pip or conda if user wants to use snip_momentum sparse pruning"
  
          from .helper import generate_pruners, register_on_step_begin
          from nc import WeightPruningConfig
  
          config = WeightPruningConfig(
              target_sparsity=1
              - shared_parameters[SPARSE_PRUNING_DENSE_RATIO],
              pattern=shared_parameters[SPARSE_PRUNING_BLOCK_PATTERN],
              pruning_frequency=shared_parameters[
                  SPARSE_PRUNING_SCHEDULE_OFFSET_STRIDE
              ],
              start_step=shared_parameters[
                  SPARSE_PRUNING_SCHEDULE_OFFSET
              ],
              end_step=shared_parameters[
                  SPARSE_PRUNING_SCHEDULE_OFFSET_END
              ],
              excluded_op_names=shared_parameters[
                  SPARSE_PRUNING_EXCLUDED_MODULES
              ],
          )
          pruners = generate_pruners(config, c_model)
          c_model.pruners = pruners
          register_on_step_begin(c_model)
  
      return model
  
file_path: DeepSpeed/deepspeed/compression/compress.py
imports: import re
  from .helper import compression_preparation, fix_compression, recursive_getattr, is_module_compressible
  from .config import get_compression_config
  from ..runtime.config_utils import dict_raise_error_on_duplicate_keys
  from .constants import *
  import os
  import json
label_desc: Use image node_id 4 for calling a global function with example usage: init_compression(model, deepspeed_config, teacher_model, mpu) and returns: model