code: def get_tensorcore_tflops(device, num_ctas, num_warps, dtype):
      """return compute throughput in TOPS"""
      total_warps = num_ctas * min(num_warps, 4)
      num_subcores = (
          driver.utils.get_device_properties(device)[
              "multiprocessor_count"
          ]
          * 4
      )  # on recent GPUs
      cur_sm_clock = nvsmi(["clocks.max.sm"])[0]
      tflops = (
          min(num_subcores, total_warps)
          / num_subcores
          * get_max_tensorcore_tflops(dtype, cur_sm_clock, device)
      )
      return tflops
  
file_path: triton/python/triton/ops/matmul_perf_model.py
imports: import heapq
  import torch
  from ..None import cdiv
  from ..runtime import driver
  from ..testing import get_dram_gbps, get_max_simd_tflops, get_max_tensorcore_tflops, nvsmi
label_desc: Use image node_id 1 for calling a global function with example usage: get_tensorcore_tflops(device, num_ctas, num_warps, dtype) and returns: tflops