code: def student_initialization(
      student_model, teacher_model, deepspeed_config
  ):
      """
      Given a student model and a teacher model, select the
      Args:
          student_model (`torch.nn.Module`)
              The model we will update weight
          teacher_model (`torch.nn.Module`)
              The model guide the student to learn
          deepspeed_config (`DeepSpeedConfig`)
              The path of ds_config
      """
      config = get_compression_config(
          check_deepspeed_config(deepspeed_config)
      )
      compress_methods = config[LAYER_REDUCTION]
  
      module_name_prefix = compress_methods[MODULE_NAME_PREFIX]
      teacher_layer = compress_methods[TEACHER_LAYER]
      student_layer = [i for i in range(len(teacher_layer))]
      other_module_name = compress_methods[OTHER_MODULE_NAME]
      """
          name_prefix (`str`)
              The prefix name before the layer #.
              Example 1: bert.encoder.layer, for BERT_base model's prefix name
              Example 2: transformer.h, for GPT-2 hugging face prefix name
          teacher_layer (`list of intergers`)
              The layer of teacher will be used for student's reinitializedion
              Example 1: [1,3,5,7,9], means we want to matches the 2nd/4th/6th/8th/10th layer of teacher to the first 5 layers of student
          student_layer (`list` or None)
              The layer of student need to be re-intiialized
              Example 1: None, means we want to reinitialize all the layers
              Example 1: [0,1,2,3,4], means  we want to reinitialize the first 5 layers
          other_module_name (`list of string`)
              The modules will be used for student's reinitializedion
              Example 1: ['bert.pooler', 'bert.embeddings', 'classifier'], means we want to apply the weight in teacher's embedding/pooler/classier module to the student
              Example 2: ['transformer.w', 'transformer.ln_f', 'lm_head'], means we want to apply the weight in teacher's embeddingn layers module to the student
      Note that teacher_layer should matches student layer
      """
      assert len(student_layer) == len(teacher_layer)
      for s_name, t_name in zip(student_layer, teacher_layer):
          s_module = recursive_getattr(
              student_model, module_name_prefix + "." + str(s_name)
          )
          t_module = recursive_getattr(
              teacher_model, module_name_prefix + "." + str(t_name)
          )
          for s_param, t_param in zip(
              s_module.parameters(), t_module.parameters()
          ):
              s_param.data.copy_(t_param.data)
      for name in other_module_name:
          s_module = recursive_getattr(student_model, name)
          t_module = recursive_getattr(teacher_model, name)
          print(name)
          for s_param, t_param in zip(
              s_module.parameters(), t_module.parameters()
          ):
              s_param.data.copy_(t_param.data)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/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 6 for calling a global function with example usage: student_initialization(student_model, teacher_model, deepspeed_config) without return types