code: def run_optimizer_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 neural network
      optimizer update 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_shape = (5, 5)
      int64_tensor_shape = (2**16, 2**16)
  
      if int64_tensor == "on":
          arg_shape = int64_tensor_shape
      else:
          arg_shape = standard_shape
  
      # Run independent tests for ops that need specific input data
      multi_mp_sgd_mom_res = run_performance_test(
          [getattr(MX_OP_MODULE, "multi_mp_sgd_mom_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args2": nd.random_normal(shape=arg_shape),
                  "args3": nd.random_normal(shape=arg_shape),
                  "lrs": 0.1,
                  "wds": 0.2,
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      multi_sgd_mom_res = run_performance_test(
          [getattr(MX_OP_MODULE, "multi_sgd_mom_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args2": nd.random_normal(shape=arg_shape),
                  "lrs": 0.1,
                  "wds": 0.2,
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      multi_sgd_res = run_performance_test(
          [getattr(MX_OP_MODULE, "multi_sgd_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "lrs": 0.1,
                  "wds": 0.2,
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      multi_mp_sgd_res = run_performance_test(
          [getattr(MX_OP_MODULE, "multi_mp_sgd_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args2": nd.random_normal(shape=arg_shape),
                  "lrs": 0.1,
                  "wds": 0.2,
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      preloaded_multi_mp_sgd_res = run_performance_test(
          [getattr(MX_OP_MODULE, "preloaded_multi_mp_sgd_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args2": nd.random_normal(shape=arg_shape),
                  "args3": nd.random_normal(shape=(1)),
                  "args4": nd.random_normal(shape=(1)),
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      preloaded_multi_sgd_mom_res = run_performance_test(
          [getattr(MX_OP_MODULE, "preloaded_multi_sgd_mom_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args2": nd.random_normal(shape=arg_shape),
                  "args3": nd.random_normal(shape=(1)),
                  "args4": nd.random_normal(shape=(1)),
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      preloaded_multi_sgd_res = run_performance_test(
          [getattr(MX_OP_MODULE, "preloaded_multi_sgd_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args4": nd.random_normal(shape=(1)),
                  "args5": nd.random_normal(shape=(1)),
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      preloaded_multi_mp_sgd_mom_res = run_performance_test(
          [getattr(MX_OP_MODULE, "preloaded_multi_mp_sgd_mom_update")],
          inputs=[
              {
                  "args0": nd.random_normal(shape=arg_shape),
                  "args1": nd.random_normal(shape=arg_shape),
                  "args2": nd.random_normal(shape=arg_shape),
                  "args3": nd.random_normal(shape=arg_shape),
                  "args4": nd.random_normal(shape=(1)),
                  "args5": nd.random_normal(shape=(1)),
                  "out": nd.random_normal(shape=arg_shape),
              }
          ],
          run_backward=False,
      )
  
      # Fetch remaining optimizer operators
      mx_optimizer_ops = get_all_optimizer_operators()
  
      # Run benchmarks
      mx_optimizer_op_results = run_op_benchmarks(
          mx_optimizer_ops,
          dtype,
          ctx,
          profiler,
          int64_tensor,
          warmup,
          runs,
      )
      return merge_map_list(
          multi_sgd_mom_res
          + multi_sgd_mom_res
          + multi_sgd_res
          + multi_mp_sgd_res
          + preloaded_multi_mp_sgd_res
          + preloaded_multi_sgd_mom_res
          + preloaded_multi_mp_sgd_res
          + preloaded_multi_mp_sgd_mom_res
          + multi_mp_sgd_mom_res
          + preloaded_multi_sgd_res
          + [mx_optimizer_op_results]
      )
  
imports: import mxnet
  from mxnet import nd
  from benchmark.opperf.utils.benchmark_utils import run_performance_test
  from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
  from benchmark.opperf.utils.op_registry_utils import get_all_optimizer_operators
  from benchmark.opperf.utils.common_utils import merge_map_list
  from benchmark.opperf.rules.default_params import MX_OP_MODULE
label_desc: Use image node_id 1 for calling a global function with example usage: run_optimizer_operators_benchmarks(ctx, dtype, profiler, int64_tensor, warmup, runs) and returns: merge_map_list