code: def run_op_benchmarks(
      ops, dtype, ctx, profiler, int64_tensor, warmup, runs
  ):
      # Running im2col either forwards or backwards on GPU results in errors
      # track issue here: https://github.com/apache/mxnet/issues/17493
      gpu_disabled_ops = ["im2col"]
  
      # For each operator, run benchmarks
      mx_op_benchmark_results = []
      for op, op_params in ops.items():
          if ctx == mx.cpu() or op not in gpu_disabled_ops:
              # Prepare inputs for the operator
              inputs = prepare_op_inputs(op, op_params, int64_tensor)
  
              # setting backward false for ops with known issue
              if op in no_backward:
                  op_params["has_backward"] = False
  
              # Run benchmarks
              cur_op_res = run_performance_test(
                  op_params["nd_op_handle"],
                  run_backward=op_params["has_backward"],
                  dtype=dtype,
                  ctx=ctx,
                  profiler=profiler,
                  inputs=inputs,
                  warmup=warmup,
                  runs=runs,
              )
              mx_op_benchmark_results += cur_op_res
  
      # Prepare combined results for all operators
      mx_op_benchmark_results = merge_map_list(mx_op_benchmark_results)
      return mx_op_benchmark_results
  
file_path: mxnet/benchmark/opperf/utils/benchmark_utils.py
imports: import logging
  import inspect
  import mxnet
  from mxnet import nd
  from mxnet import np
  from .ndarray_utils import get_mx_ndarray, nd_forward_and_profile, nd_forward_backward_and_profile
  from .common_utils import merge_map_list
  from .op_registry_utils import prepare_op_inputs
  from benchmark.opperf.rules.default_params import PARAMS_OF_TYPE_NDARRAY, PARAMS_OF_TYPE_NP_ARRAY
  from .profiler_utils import cpp_profile, python_profile
label_desc: Use image node_id 8 for calling a global function with example usage: run_op_benchmarks(ops, dtype, ctx, profiler, int64_tensor, warmup, runs) and returns: mx_op_benchmark_results