code: def load_wkv_kernel(context_size: int) -> None:
      """Load WKV CUDA kernel.
  
      Args:
          context_size: Context size.
  
      """
      from torch.utils.cpp_extension import load
  
      global wkv_kernel
  
      if (
          wkv_kernel is not None
          and wkv_kernel.context_size == context_size
      ):
          return
  
      if find_spec("ninja") is None:
          raise ImportError(
              "Ninja package was not found. WKV kernel module can't be loaded "
              "for training. Please, 'pip install ninja' in your environment."
          )
  
      if not torch.cuda.is_available():
          raise ImportError(
              "CUDA is currently a requirement for WKV kernel loading. "
              "Please set your devices properly and launch again."
          )
  
      kernel_folder = Path(__file__).resolve().parent / "cuda"
      kernel_files = [
          kernel_folder / f for f in ["wkv_op.cpp", "wkv_cuda.cu"]
      ]
  
      kernel_cflags = [
          "-t 4",
          "-std=c++17",
          "-res-usage",
          "--maxrregcount 60",
          "--use_fast_math",
          "-O3",
          "-Xptxas -O3",
          "--extra-device-vectorization",
          f"-DTmax={context_size}",
      ]
  
      wkv_kernel = load(
          name=f"wkv_{context_size}",
          sources=kernel_files,
          verbose=False,
          extra_cuda_cflags=kernel_cflags,
      )
      wkv_kernel.context_size = context_size
  
file_path: espnet/espnet2/asr_transducer/decoder/modules/rwkv/attention.py
imports: import math
  from importlib.util import find_spec
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: load_wkv_kernel(context_size) without return types