code: def _merge_dicts_and_lists_b_into_a(a, b, cur_key=None):
      """The function is inspired by mmcf.Config._merge_a_into_b.
  
      * works with usual dicts and lists and derived types
      * supports merging of lists (by concatenating the lists)
      * makes recursive merging for dict + dict case
      * overwrites when merging scalar into scalar
      Note that we merge b into a (whereas Config makes merge a into b),
      since otherwise the order of list merging is counter-intuitive.
      """
  
      def _err_str(_a, _b, _key):
          if _key is None:
              _key_str = "of whole structures"
          else:
              _key_str = f"during merging for key=`{_key}`"
          return (
              f"Error in merging parts of config: different types {_key_str},"
              f" type(a) = {type(_a)},"
              f" type(b) = {type(_b)}"
          )
  
      assert isinstance(
          a, (dict, list)
      ), f"Can merge only dicts and lists, whereas type(a)={type(a)}"
      assert isinstance(b, (dict, list)), _err_str(a, b, cur_key)
      assert isinstance(a, list) == isinstance(b, list), _err_str(
          a, b, cur_key
      )
      if isinstance(a, list):
          # the main diff w.r.t. mmcf.Config -- merging of lists
          return a + b
  
      a = copy(a)
      for k in b.keys():
          if k not in a:
              a[k] = copy(b[k])
              continue
          new_cur_key = cur_key + "." + k if cur_key else k
          if isinstance(a[k], (dict, list)):
              a[k] = _merge_dicts_and_lists_b_into_a(
                  a[k], b[k], new_cur_key
              )
              continue
  
          assert not isinstance(b[k], (dict, list)), _err_str(
              a[k], b[k], new_cur_key
          )
  
          # suppose here that a[k] and b[k] are scalars, just overwrite
          a[k] = b[k]
      return a
  
imports: from __future__ import annotations
  import logging
  from copy import copy
  from typing import Any, Iterator
  from nncf import NNCFConfig
  from nncf.api.compression import CompressionAlgorithmController
  from nncf.torch import create_compressed_model, load_state, register_default_init_args
  from nncf.torch.initialization import PTInitializingDataLoader
  from nncf.torch.nncf_network import NNCFNetwork
  from torch import nn
  from torch.utils.data.dataloader import DataLoader
label_desc: Use image node_id 5 for calling a global function with example usage: _merge_dicts_and_lists_b_into_a(a, b, cur_key) and returns: a, str, _key_str, str, type, str, type, unknown