code: def run_mx_misc_operators_benchmarks(
      ctx=mx.cpu(),
      dtype="float32",
      profiler="native",
      int64_tensor="off",
      warmup=25,
      runs=100,
  ):
      """Runs benchmarks with the given context and precision (dtype) for all the miscellaneous
      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.
  
      """
  
      standard_inputs_array_ops = [
          {"args": [(1024, 1024)], "num_arrays": 1},
          {"args": [(10000, 1)], "num_arrays": 1},
          {"args": [(10000, 10)], "num_arrays": 1},
      ]
      int64_tensor_inputs_array_ops = [
          {"args": [(2**32, 1)], "num_arrays": 1}
      ]
      standard_inputs_add_n = [
          {"args": [(1024, 1024)]},
          {"args": [(10000, 1)]},
          {"args": [(10000, 10)]},
      ]
      int64_tensor_inputs_add_n = [{"args": [(2**16, 2**16)]}]
      standard_inputs_upsampling = [
          {
              "args": (32, 3, 256, 256),
              "scale": 2,
              "sample_type": "nearest",
          },
          {
              "args": (32, 3, 10000, 1),
              "scale": 4,
              "sample_type": "nearest",
          },
      ]
      int64_tensor_inputs_upsampling = [
          {
              "args": (2**32 + 1, 1, 1, 1),
              "scale": 2,
              "sample_type": "nearest",
          }
      ]
      standard_inputs_custom = [
          {"args": [(1024, 1024)], "op_type": "CustomAddOne"},
          {"args": [(10000, 1)], "op_type": "CustomAddOne"},
          {"args": [(10000, 10)], "op_type": "CustomAddOne"},
      ]
      int64_tensor_inputs_custom = [
          {"args": [(2**32 + 1, 1)], "op_type": "CustomAddOne"}
      ]
  
      if int64_tensor == "on":
          inputs_array_ops = int64_tensor_inputs_array_ops
          inputs_add_n = int64_tensor_inputs_add_n
          inputs_upsampling = int64_tensor_inputs_upsampling
          inputs_custom = int64_tensor_inputs_custom
      else:
          inputs_array_ops = standard_inputs_array_ops
          inputs_add_n = standard_inputs_add_n
          inputs_upsampling = standard_inputs_upsampling
          inputs_custom = standard_inputs_custom
  
      # Individual tests for ops with positional args
      array_ops_benchmark = run_performance_test(
          [
              getattr(MX_OP_MODULE, "reset_arrays"),
              getattr(MX_OP_MODULE, "multi_all_finite"),
              getattr(MX_OP_MODULE, "multi_sum_sq"),
          ],
          run_backward=False,
          dtype=dtype,
          ctx=ctx,
          profiler=profiler,
          inputs=inputs_array_ops,
          warmup=warmup,
          runs=runs,
      )
      add_n_benchmark = run_performance_test(
          [getattr(MX_OP_MODULE, "add_n")],
          run_backward=True,
          dtype=dtype,
          ctx=ctx,
          profiler=profiler,
          inputs=inputs_add_n,
          warmup=warmup,
          runs=runs,
      )
      # There are currently issus with UpSampling with bilinear interpolation.
      # track issue here: https://github.com/apache/mxnet/issues/9138
      upsampling_benchmark = run_performance_test(
          [getattr(MX_OP_MODULE, "UpSampling")],
          run_backward=True,
          dtype=dtype,
          ctx=ctx,
          profiler=profiler,
          inputs=inputs_upsampling,
          warmup=warmup,
          runs=runs,
      )
      # Create and register CustomAddOne operator for use in Custom op testing
      c = CustomAddOneProp()
      c.create_operator(ctx, [(1024, 1024)], [dtype])
      custom_benchmark = run_performance_test(
          [getattr(MX_OP_MODULE, "Custom")],
          run_backward=True,
          dtype=dtype,
          ctx=ctx,
          profiler=profiler,
          inputs=inputs_custom,
          warmup=warmup,
          runs=runs,
      )
  
      # Fetch remaining Miscellaneous Operators
      mx_misc_ops = get_remaining_miscellaneous_operators()
      # Run benchmarks
      mx_misc_op_results = run_op_benchmarks(
          mx_misc_ops, dtype, ctx, profiler, int64_tensor, warmup, runs
      )
      return merge_map_list(
          array_ops_benchmark
          + add_n_benchmark
          + upsampling_benchmark
          + custom_benchmark
          + [mx_misc_op_results]
      )
  
imports: import mxnet
  from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
  from benchmark.opperf.utils.op_registry_utils import get_remaining_miscellaneous_operators
  from benchmark.opperf.utils.benchmark_utils import run_performance_test
  from benchmark.opperf.utils.common_utils import merge_map_list
  from benchmark.opperf.rules.default_params import MX_OP_MODULE
  from benchmark.opperf.custom_operations.custom_operations import CustomAddOneProp
label_desc: Use image node_id 1 for calling a global function with example usage: run_mx_misc_operators_benchmarks(ctx, dtype, profiler, int64_tensor, warmup, runs) and returns: merge_map_list