code: def create_student_by_copying_alternating_layers(
      teacher: Union[str, PreTrainedModel],
      save_path: Union[str, Path] = "student",
      e: Union[int, None] = None,
      d: Union[int, None] = None,
      copy_first_teacher_layers=False,
      e_layers_to_copy=None,
      d_layers_to_copy=None,
      **extra_config_kwargs,
  ) -> Tuple[PreTrainedModel, List[int], List[int]]:
      """Make a student by copying alternating layers from a teacher, save it to save_path.
      Args:
          teacher: str or PreTrainedModel if str, this will call AutoModelForSeq2SeqLM.from_pretrained(teacher) before
          copying layers
          save_path: where to save the student, defaults to student directory.
          e: how many Encoder layers should the student have, default is fully copy of teacher
          d: how many Decoder layers should the student have, default is fully copy of teacher
          copy_first_teacher_layers: [bool] dont copy alternating layers, just the first e/d.
          **extra_config_kwargs: extra kwargs to pass to the student, by default the teacher config is used.
  
      Returns:
          student: new, smaller model.  (Also saves it to save_path)
          e_layers_to_copy: list of which teacher encoder layers were used
          d_layers_to_copy: list of which teacher decoder layers were used
      """
      _msg = "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
      assert (e is not None) or (d is not None), _msg
      if isinstance(teacher, str):
          AutoTokenizer.from_pretrained(teacher).save_pretrained(
              save_path
          )  # purely for convenience
          teacher = AutoModelForSeq2SeqLM.from_pretrained(
              teacher
          ).eval()
      else:
          assert isinstance(
              teacher, PreTrainedModel
          ), f"teacher must be a model or string got type {type(teacher)}"
      init_kwargs = teacher.config.to_diff_dict()
  
      try:
          teacher_e, teacher_d = (
              teacher.config.encoder_layers,
              teacher.config.decoder_layers,
          )
          if e is None:
              e = teacher_e
          if d is None:
              d = teacher_d
          init_kwargs.update({"encoder_layers": e, "decoder_layers": d})
      except AttributeError:  # T5
          if hasattr(teacher.config, "num_encoder_layers"):
              teacher_e, teacher_d = (
                  teacher.config.num_encoder_layers,
                  teacher.config.num_decoder_layers,
              )
          else:
              teacher_e, teacher_d = (
                  teacher.config.num_layers,
                  teacher.config.num_decoder_layers,
              )
          if e is None:
              e = teacher_e
          if d is None:
              d = teacher_d
          if hasattr(teacher.config, "num_encoder_layers"):
              init_kwargs.update(
                  {"num_encoder_layers": e, "num_decoder_layers": d}
              )
          else:
              init_kwargs.update(
                  {"num_layers": e, "num_decoder_layers": d}
              )
  
      # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
      init_kwargs.update(extra_config_kwargs)
  
      # Copy weights
      student_cfg = teacher.config_class(**init_kwargs)
      student = AutoModelForSeq2SeqLM.from_config(student_cfg)
      # Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
      info = student.load_state_dict(teacher.state_dict(), strict=False)
      assert (
          info.missing_keys == []
      ), (
          info.missing_keys
      )  # every student key should have a teacher keys.
  
      if (
          copy_first_teacher_layers
      ):  # Our copying is done. We just log and save
          e_layers_to_copy, d_layers_to_copy = list(range(e)), list(
              range(d)
          )
          logger.info(
              f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
              f" {save_path}"
          )
          student.save_pretrained(save_path)
          return student, e_layers_to_copy, d_layers_to_copy
  
      # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
      if e_layers_to_copy is None:
          e_layers_to_copy: List[int] = pick_layers_to_copy(
              e, teacher_e
          )
      if d_layers_to_copy is None:
          d_layers_to_copy: List[int] = pick_layers_to_copy(
              d, teacher_d
          )
  
      try:
          if hasattr(
              teacher, "prophetnet"
          ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
              copy_layers(
                  teacher.prophetnet.encoder.layers,
                  student.prophetnet.encoder.layers,
                  e_layers_to_copy,
              )
              copy_layers(
                  teacher.prophetnet.decoder.layers,
                  student.prophetnet.decoder.layers,
                  d_layers_to_copy,
              )
          else:
              copy_layers(
                  teacher.model.encoder.layers,
                  student.model.encoder.layers,
                  e_layers_to_copy,
              )
              copy_layers(
                  teacher.model.decoder.layers,
                  student.model.decoder.layers,
                  d_layers_to_copy,
              )
      except (
          AttributeError
      ):  # For t5, student.model.encoder.layers is called student.encoder.block
          copy_layers(
              teacher.encoder.block,
              student.encoder.block,
              e_layers_to_copy,
          )
          copy_layers(
              teacher.decoder.block,
              student.decoder.block,
              d_layers_to_copy,
          )
      logger.info(
          f"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"
      )
      student.config.init_metadata = {
          "teacher_type": teacher.config.model_type,
          "copied_encoder_layers": e_layers_to_copy,
          "copied_decoder_layers": d_layers_to_copy,
      }
      student.save_pretrained(save_path)
      # Save information about copying for easier reproducibility
  
      return student, e_layers_to_copy, d_layers_to_copy
  
file_path: transformers/examples/research_projects/seq2seq-distillation/make_student.py
imports: import warnings
  from pathlib import Path
  from typing import List, Tuple, Union
  import fire
  from torch import nn
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: create_student_by_copying_alternating_layers(teacher, save_path, e, d, copy_first_teacher_layers, e_layers_to_copy, d_layers_to_copy) and returns: student,  e_layers_to_copy,  d_layers_to_copy, student,  e_layers_to_copy,  d_layers_to_copy