code: def assign_non_encoder_layer_ids(
      non_encoder_names: List[str],
      layer_id: int,
  ):
      """
      Assign the provided id to non-encoder layers.
  
      Parameters
      ----------
      non_encoder_names
          Names layers not belonging to an encoder.
      layer_id
          provided id.
  
      Returns
      -------
      A dictionary mapping the layer names (keys) to their ids (values).
      """
      name_to_id = {}
      for n in non_encoder_names:
          name_to_id[n] = layer_id
      return name_to_id
  
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 3 for calling a global function with example usage: assign_non_encoder_layer_ids(non_encoder_names, layer_id) and returns: name_to_id