code: def assign_layer_ids(
      names: List[str],
      pre_encoder_patterns: Tuple[str, ...],
      post_encoder_patterns: Tuple[str, ...],
      model_pre: Optional[str] = None,
  ):
      """
      Assign ids to all layers. It splits a model into three parts: pre-encoder, encoder, and post-encoder.
      Encoder is generally a stack of multiple similar layers, such as transformer layers. Since encoder is
      generally wrapped by nn.Sequential() or nn.ModuleList(), its inside layer names contain digits.
      It sets 0 as the ids of all post-encoder layers and a maximum id (layer_num) for the all the pre-encoder
      layers. The encoder layers have decreasing ids from the input to the output ends.
  
      Parameters
      ----------
      names
          model layer names.
      pre_encoder_patterns
          Patterns to identify a layer as a pre-encoder layer. If a layer name contains one pattern,
          the layer will be grouped into pre-encoder layers.
      post_encoder_patterns
          Patterns to identify a layer as a post-encoder layer. If a layer name contains one pattern,
          the layer will be grouped into post-encoder layers.
      model_pre
          The layer names' prefix. Only the layer names with this prefix will be assigned ids. The left
          layer names will be returned.
  
      Returns
      -------
      name_to_id
          A dictionary mapping the layer names (keys) to their ids (values).
      left_names
          The layer names not starting with the "model_pre".
      """
      try:
          (
              left_names,
              encoder_names,
              pre_encoder_names,
              post_encoder_names,
          ) = group_param_names(
              names=names,
              pre_encoder_patterns=pre_encoder_patterns,
              post_encoder_patterns=post_encoder_patterns,
              model_prefix=model_pre,
          )
          # add a constraint
          if len(encoder_names) == 0 and len(pre_encoder_names) != 0:
              raise ValueError(
                  f"encoder_names is empty, but pre_encoder_names has values: {pre_encoder_names}"
              )
  
          (
              encoder_name_to_id,
              encoder_layer_num,
          ) = assign_encoder_layer_ids(
              encoder_names=encoder_names,
          )
  
          pre_encoder_name_to_id = assign_non_encoder_layer_ids(
              non_encoder_names=pre_encoder_names, layer_id=0
          )
  
          post_encoder_name_to_id = assign_non_encoder_layer_ids(
              non_encoder_names=post_encoder_names,
              layer_id=encoder_layer_num + 1,
          )
  
          name_to_id = reverse_layer_ids(
              encoder_name_to_id=encoder_name_to_id,
              pre_enocder_name_to_id=pre_encoder_name_to_id,
              post_enocder_name_to_id=post_encoder_name_to_id,
          )
      except Exception as e:
          logger.debug(
              f"When calling assign_layer_ids(), it catches exception: {e}. All the layers will use the same layer_id."
          )
          name_to_id = dict()
          left_names = names
  
      return name_to_id, left_names
  
imports: import logging
  import re
  import warnings
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch._dynamo
  import torch.nn.functional
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from transformers import AutoConfig, AutoModel, AutoTokenizer, BertTokenizer, CLIPTokenizer, ElectraTokenizer
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerLoss
  from ..constants import AUTOMM, CLASS_LOGITS, COLUMN_FEATURES, FEATURES, LOGITS, MASKS, OCR, PEFT_ADDITIVE_STRATEGIES, REGRESSION, SEMANTIC_MASK, SEMANTIC_SEGMENTATION
  from .adaptation_layers import IA3Linear, IA3LoRALinear, LoRALinear
label_desc: Use image node_id 7 for calling a global function with example usage: assign_layer_ids(names, pre_encoder_patterns, post_encoder_patterns, model_pre) and returns: name_to_id,  left_names