code: def convert_to_trt(
      model: nn.Module,
      precision: str,
      input_shape: Sequence[int],
      dynamic_batchsize: Sequence[int] | None = None,
      use_trace: bool = False,
      filename_or_obj: Any | None = None,
      verify: bool = False,
      device: int | None = None,
      use_onnx: bool | None = False,
      onnx_input_names: Sequence[str] | None = ("input_0",),
      onnx_output_names: Sequence[str] | None = ("output_0",),
      rtol: float = 1e-2,
      atol: float = 0.0,
      **kwargs,
  ):
      """
      Utility to export a model into a TensorRT engine-based TorchScript model with optional input / output data verification.
  
      There are two ways to export a model:
      1, Torch-TensorRT way: PyTorch module ---> TorchScript module ---> TensorRT engine-based TorchScript.
      2, ONNX-TensorRT way: PyTorch module ---> TorchScript module ---> ONNX model ---> TensorRT engine --->
      TensorRT engine-based TorchScript.
  
      When exporting through the first way, some models suffer from the slowdown problem, since Torch-TensorRT
      may only convert a little part of the PyTorch model to the TensorRT engine. However when exporting through
      the second way, some Python data structures like `dict` are not supported. And some TorchScript models are
      not supported by the ONNX if exported through `torch.jit.script`.
  
      Args:
          model: a source PyTorch model to convert.
          precision: the weight precision of the converted TensorRT engine based TorchScript models. Should be 'fp32' or 'fp16'.
          input_shape: the input shape that is used to convert the model. Should be a list like [N, C, H, W] or
              [N, C, H, W, D].
          dynamic_batchsize: a sequence with three elements to define the batch size range of the input for the model to be
              converted. Should be a sequence like [MIN_BATCH, OPT_BATCH, MAX_BATCH]. After converted, the batchsize of model
              input should between `MIN_BATCH` and `MAX_BATCH` and the `OPT_BATCH` is the best performance batchsize that the
              TensorRT tries to fit. The `OPT_BATCH` should be the most frequently used input batchsize in the application,
              default to None.
          use_trace: whether using `torch.jit.trace` to convert the PyTorch model to a TorchScript model and then convert to
              a TensorRT engine based TorchScript model or an ONNX model (if `use_onnx` is True), default to False.
          filename_or_obj: if not None, specify a file-like object (has to implement write and flush) or a string containing a
              file path name to load the TensorRT engine based TorchScript model for verifying.
          verify: whether to verify the input and output of the TensorRT engine based TorchScript model.
          device: the target GPU index to convert and verify the model. If None, use #0 GPU.
          use_onnx: whether to use the ONNX-TensorRT way to export the TensorRT engine-based TorchScript model.
          onnx_input_names: optional input names of the ONNX model. This arg is only useful when `use_onnx` is True. Should be
              a sequence like `('input_0', 'input_1', ..., 'input_N')` where N equals to the number of the model inputs. If not
              given, will use `('input_0',)`, which supposes the model only has one input.
          onnx_output_names: optional output names of the ONNX model. This arg is only useful when `use_onnx` is True. Should be
              a sequence like `('output_0', 'output_1', ..., 'output_N')` where N equals to the number of the model outputs. If
              not given, will use `('output_0',)`, which supposes the model only has one output.
          rtol: the relative tolerance when comparing the outputs between the PyTorch model and TensorRT model.
          atol: the absolute tolerance when comparing the outputs between the PyTorch model and TensorRT model.
          kwargs: other arguments except `module`, `inputs`, `enabled_precisions` and `device` for `torch_tensorrt.compile()`
              to compile model, for more details: https://pytorch.org/TensorRT/py_api/torch_tensorrt.html#torch-tensorrt-py.
      """
  
      torch_tensorrt, _ = optional_import(
          "torch_tensorrt", version="1.4.0"
      )
  
      if not torch.cuda.is_available():
          raise Exception("Cannot find any GPU devices.")
  
      if not input_shape:
          raise ValueError("Missing the input shape for model convert.")
  
      if not dynamic_batchsize:
          warnings.warn(
              f"There is no dynamic batch range. The converted model only takes {input_shape} shape input."
          )
  
      if (dynamic_batchsize is not None) and (
          len(dynamic_batchsize) != 3
      ):
          warnings.warn(
              f"The dynamic batch range sequence should have 3 elements, but got {dynamic_batchsize} elements."
          )
  
      device = device if device else 0
      target_device = (
          torch.device(f"cuda:{device}")
          if device
          else torch.device("cuda:0")
      )
      convert_precision = (
          torch.float32 if precision == "fp32" else torch.half
      )
      inputs = [torch.rand(ensure_tuple(input_shape)).to(target_device)]
  
      def scale_batch_size(input_shape: Sequence[int], scale_num: int):
          scale_shape = [*input_shape]
          scale_shape[0] *= scale_num
          return scale_shape
  
      # Use the dynamic batchsize range to generate the min, opt and max model input shape
      if dynamic_batchsize:
          min_input_shape = scale_batch_size(
              input_shape, dynamic_batchsize[0]
          )
          opt_input_shape = scale_batch_size(
              input_shape, dynamic_batchsize[1]
          )
          max_input_shape = scale_batch_size(
              input_shape, dynamic_batchsize[2]
          )
      else:
          min_input_shape = (
              opt_input_shape
          ) = max_input_shape = input_shape
  
      # convert the torch model to a TorchScript model on target device
      model = model.eval().to(target_device)
      ir_model = convert_to_torchscript(
          model,
          device=target_device,
          inputs=inputs,
          use_trace=use_trace,
      )
      ir_model.eval()
  
      if use_onnx:
          # set the batch dim as dynamic
          dynamic_axes = (
              {k: {0: "batchsize"} for k in onnx_input_names}
              if onnx_input_names
              else {}
          )
          dynamic_axes.update(
              {k: {0: "batchsize"} for k in onnx_output_names}
              if onnx_output_names
              else {}
          )
          ir_model = convert_to_onnx(
              model,
              inputs,
              onnx_input_names,
              onnx_output_names,
              use_trace=use_trace,
              dynamic_axes=dynamic_axes,
          )
  
          # convert the model through the ONNX-TensorRT way
          trt_model = _onnx_trt_compile(
              ir_model,
              min_shape=min_input_shape,
              opt_shape=opt_input_shape,
              max_shape=max_input_shape,
              device=device,
              precision=precision,
              input_names=onnx_input_names,
              output_names=onnx_output_names,
          )
      else:
          # convert the model through the Torch-TensorRT way
          ir_model.to(target_device)
          with torch.no_grad():
              with torch.cuda.device(device=device):
                  input_placeholder = [
                      torch_tensorrt.Input(
                          min_shape=min_input_shape,
                          opt_shape=opt_input_shape,
                          max_shape=max_input_shape,
                      )
                  ]
                  trt_model = torch_tensorrt.compile(
                      ir_model,
                      inputs=input_placeholder,
                      enabled_precisions=convert_precision,
                      device=target_device,
                      **kwargs,
                  )
  
      # verify the outputs between the TensorRT model and PyTorch model
      if verify:
          if inputs is None:
              raise ValueError("Missing input data for verification.")
  
          trt_model = (
              torch.jit.load(filename_or_obj)
              if filename_or_obj is not None
              else trt_model
          )
  
          with torch.no_grad():
              set_determinism(seed=0)
              torch_out = ensure_tuple(model(*inputs))
              set_determinism(seed=0)
              trt_out = ensure_tuple(trt_model(*inputs))
              set_determinism(seed=None)
          # compare TorchScript and PyTorch results
          for r1, r2 in zip(torch_out, trt_out):
              if isinstance(r1, torch.Tensor) or isinstance(
                  r2, torch.Tensor
              ):
                  assert_fn = (
                      torch.testing.assert_close
                      if pytorch_after(1, 11)
                      else torch.testing.assert_allclose
                  )
                  assert_fn(r1, r2, rtol=rtol, atol=atol)  # type: ignore
  
      return trt_model
  
imports: from __future__ import annotations
  import io
  import re
  import warnings
  from collections import OrderedDict
  from collections.abc import Callable, Mapping, Sequence
  from contextlib import contextmanager
  from copy import deepcopy
  from typing import Any
  import numpy
  import torch
  import torch.nn
  from monai.apps.utils import get_logger
  from monai.config import PathLike
  from monai.utils.misc import ensure_tuple, save_obj, set_determinism
  from monai.utils.module import look_up_option, optional_import, pytorch_after
  from monai.utils.type_conversion import convert_to_dst_type, convert_to_tensor
label_desc: Use image node_id 19 for calling a global function with example usage: convert_to_trt(model, precision, input_shape, dynamic_batchsize, use_trace, filename_or_obj, verify, device, use_onnx, onnx_input_names, onnx_output_names, rtol, atol) and returns: trt_model, scale_shape