code: def jit_load(self, verbose=True):
      if not self.is_compatible():
          raise RuntimeError(
              f"Unable to JIT load the {self.name} op due to it not being compatible due to hardware/software issue."
          )
      try:
          import ninja
      except ImportError:
          raise RuntimeError(
              f"Unable to JIT load the {self.name} op due to ninja not being installed."
          )
  
      if isinstance(self, CUDAOpBuilder):
          assert_no_cuda_mismatch()
  
      self.jit_mode = True
      from torch.utils.cpp_extension import load
  
      # Ensure directory exists to prevent race condition in some cases
      ext_path = os.path.join(
          os.environ.get(
              "TORCH_EXTENSIONS_DIR", DEFAULT_TORCH_EXTENSION_PATH
          ),
          self.name,
      )
      os.makedirs(ext_path, exist_ok=True)
  
      start_build = time.time()
      op_module = load(
          name=self.name,
          sources=[self.abs_src_path(path) for path in self.sources()],
          extra_include_paths=[
              self.abs_src_path(path) for path in self.include_paths()
          ],
          extra_cflags=self.cxx_args(),
          extra_cuda_cflags=self.nvcc_args(),
          extra_ldflags=["-L" + path for path in self.library_dirs()]
          + ["-l" + path for path in self.dynamic_libraries()]
          + ["-Wl,--start-group"]
          + self.static_libraries()
          + ["-Wl,--end-group"],
          verbose=verbose,
      )
      build_duration = time.time() - start_build
      if verbose:
          print(
              f"Time to load {self.name} op: {build_duration} seconds"
          )
      return op_module
  
imports: import os
  import time
  import torch
  import importlib
  from pathlib import Path
  import subprocess
  from abc import ABC, abstractmethod
label_desc: Use image node_id 17 for calling the OpBuilder obj's underlying member method code with example usage: obj.jit_load(verbose) and returns: op_module