code: def _compile_model(
      self,
      onnx_model_path: str,
      model_params: ModelParams,
      config,
      network,
      builder,
      nvidia_logger,
  ):
      parser = trt.OnnxParser(network, nvidia_logger)
      success = parser.parse_from_file(onnx_model_path)
  
      if not success:
          for idx in range(parser.num_errors):
              self.logger.debug(parser.get_error(idx))
          raise ValueError(
              f"Errors occurred while processing the "
              f"ONNX file at {onnx_model_path}"
          )
  
      if model_params.dynamic_info is not None:
          inputs_shapes = self._extract_dynamic_shape_ranges(
              model_params
          )
          profile = builder.create_optimization_profile()
          for i, input_name in enumerate(
              get_input_names(onnx_model_path)
          ):
              profile.set_shape(
                  input_name,
                  inputs_shapes[i]["min_shape"],
                  inputs_shapes[i]["opt_shape"],
                  inputs_shapes[i]["max_shape"],
              )
          config.add_optimization_profile(profile)
      return builder.build_serialized_network(network, config)
  
imports: import abc
  import copy
  import os
  import subprocess
  from pathlib import Path
  from typing import List, Any, Tuple
  import numpy
  from nebullvm.config import QUANTIZATION_DATA_NUM, TORCH_TENSORRT_PRECISIONS
  from nebullvm.core.models import QuantizationType, ModelParams
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.operations.optimizations.compilers.quantizations.tensor_rt import quantize_tensorrt
  from nebullvm.operations.optimizations.compilers.quantizations.utils import check_quantization
  from nebullvm.optional_modules.onnx import onnx
  from nebullvm.optional_modules.tensor_rt import tensorrt
  from nebullvm.optional_modules.torch import torch, Module
  from nebullvm.optional_modules.torch_tensorrt import torch_tensorrt, DataLoaderCalibrator
  from nebullvm.tools.data import DataManager, PytorchDataset
  from nebullvm.tools.diffusers import UNet
  from nebullvm.tools.onnx import get_input_names
  from nebullvm.tools.transformations import MultiStageTransformation, HalfPrecisionTransformation
label_desc: Use image node_id 3 for calling the ONNXTensorRTCompiler obj's underlying member method code with example usage: obj._compile_model(onnx_model_path, model_params, config, network, builder, nvidia_logger) and returns: builder