code: def just_tensorrt(model):
      """Run partition_for_tensorrt, complete the compilation with TVM, and profile the result."""
      logging.info(f"just_tensorrt | {model['name']}")
      logging.info("-------------- BEGIN ORIGINAL --------------")
      logging.info(model["mod"])
      logging.info("-------------- END ORIGINAL ----------------")
      tmp_dir = tempfile.mkdtemp()
      autotvm_tune_module(model["mod"], CUDA, TUNING_LOG)
      with optional_tuning_records(TUNING_LOG):
          logging.info("Partitioning for TensorRT...")
          use_fp16 = model["main_dtype"] == "float16"
          trt_target = tvm.target.Target(
              f"tensorrt -use_implicit_batch=False -use_fp16={use_fp16}",
              HOST,
          )
          mod = tvm.relay.op.contrib.partition_for_tensorrt(
              mod=model["mod"],
              params=model["params"],
              target=trt_target,
          )
          partitioned_model = model.copy()
          partitioned_model["mod"] = mod
          logging.info(
              "-------------- BEGIN PARTITIONED --------------"
          )
          logging.info(partitioned_model["mod"])
          logging.info(
              "-------------- END PARTITIONED ----------------"
          )
          targets = []
          targets.append(CUDA)
          targets.append(trt_target)
          dev = tvm.device(CUDA.get_target_device_type())
          compile_and_benchmark(
              "just_tensorrt", partitioned_model, targets, dev, tmp_dir
          )
  
imports: import tvm
  import logging
  import tempfile
  import os
  import shutil
  import menangerie
  from tvm.relay.op.contrib.cutlass import partition_for_cutlass
  from tvm.contrib.cutlass import num_cutlass_partitions
  from tvm.relay.op.contrib.cublas import partition_for_cublas
  from tvm.relay.op.contrib.cudnn import partition_for_cudnn
label_desc: Use image node_id 8 for calling a global function with example usage: just_tensorrt(model) without return types