code: def run_sorting_searching_operators_benchmarks(
      ctx=mx.cpu(),
      dtype="float32",
      profiler="native",
      int64_tensor="off",
      warmup=25,
      runs=100,
  ):
      """Runs benchmarks with the given context, precision (dtype), and input data size (int64_tensor) for all the sorting and searching
      operators in MXNet.
  
      Parameters
      ----------
      ctx: mx.ctx
          Context to run benchmarks
      dtype: str, default 'float32'
          Precision to use for benchmarks
      profiler: str, default 'native'
          Type of Profiler to use (native/python)
      int64_tensor: str, default 'off'
          Input tensor size to use for tests (if on, dimensions >= 2**32)
      warmup: int, default 25
          Number of times to run for warmup
      runs: int, default 100
          Number of runs to capture benchmark results
  
      Returns
      -------
      Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
  
      """
      # Fetch all Random Sampling Operators
      mx_sort_search_ops = get_all_sorting_searching_operators()
      # Run benchmarks
      mx_sort_search_op_results = run_op_benchmarks(
          mx_sort_search_ops,
          dtype,
          ctx,
          profiler,
          int64_tensor,
          warmup,
          runs,
      )
      return mx_sort_search_op_results
  
imports: import mxnet
  from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
  from benchmark.opperf.utils.op_registry_utils import get_all_sorting_searching_operators
label_desc: Use image node_id 1 for calling a global function with example usage: run_sorting_searching_operators_benchmarks(ctx, dtype, profiler, int64_tensor, warmup, runs) and returns: mx_sort_search_op_results