code: def run_model(
      model: nn.Module,
      batch: dict,
      trt_model: Optional[nn.Module] = None,
  ):
      from ..utils.onnx import OnnxModule
      from .document_transformer import DocumentTransformer
      from .fusion.fusion_mlp import MultimodalFusionMLP
      from .huggingface_text import HFAutoModelForTextPrediction
      from .t_few import TFewModel
      from .timm_image import TimmAutoModelForImagePrediction
  
      supported_models = (
          TimmAutoModelForImagePrediction,
          HFAutoModelForTextPrediction,
          MultimodalFusionMLP,
          TFewModel,
          OnnxModule,
      )
      pure_model = model
      if isinstance(model, torch._dynamo.eval_frame.OptimizedModule):
          pure_model = model._orig_mod
      if isinstance(model, nn.DataParallel):
          pure_model = model.module
      if isinstance(pure_model, OnnxModule):
          for k in batch:
              # HACK input data types in ONNX
              if batch[k].dtype == torch.int32:
                  batch[k] = batch[k].to(torch.int64)
      if (
          not isinstance(pure_model, DocumentTransformer)
      ) and isinstance(pure_model, supported_models):
          input_vec = [batch[k] for k in pure_model.input_keys]
          column_names, column_values = [], []
          for k in batch.keys():
              has_image_column_prefix = isinstance(
                  pure_model, TimmAutoModelForImagePrediction
              ) and k.startswith(pure_model.image_column_prefix)
              has_text_column_prefix = isinstance(
                  pure_model, HFAutoModelForTextPrediction
              ) and k.startswith(pure_model.text_column_prefix)
              if has_image_column_prefix or has_text_column_prefix:
                  column_names.append(k)
                  column_values.append(batch[k])
          if column_names != [] and column_values != []:
              input_vec.append(column_names)
              input_vec.append(column_values)
          if isinstance(pure_model, OnnxModule):
              # OnnxModule doesn't support multi-gpu yet
              output_vec = pure_model(*tuple(input_vec))
          else:
              output_vec = model(*tuple(input_vec))
  
          output = pure_model.get_output_dict(*output_vec)
      else:
          output = model(batch)
      return output
  
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 19 for calling a global function with example usage: run_model(model, batch, trt_model) and returns: output