code: def run_mx_unary_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 unary
      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 = [
          {"args": [(1024, 1024)], "num_outputs": 1},
          {"args": [(10000, 1)], "num_outputs": 1},
      ]
      int64_tensor_inputs = [{"args": [(2**32, 1)], "num_outputs": 1}]
  
      if int64_tensor == "on":
          inputs = int64_tensor_inputs
      else:
          inputs = standard_inputs
  
      # Run amp_multicast as it needs data as positional argument
      amp_multicast_benchmark = run_performance_test(
          [getattr(MX_OP_MODULE, "amp_multicast")],
          run_backward=True,
          dtype=dtype,
          ctx=ctx,
          profiler=profiler,
          inputs=inputs,
          warmup=warmup,
          runs=runs,
      )
  
      # Fetch all Unary Operators
      mx_unary_broadcast_ops = get_all_unary_operators()
  
      # Run benchmarks
      mx_unary_op_results = run_op_benchmarks(
          mx_unary_broadcast_ops,
          dtype,
          ctx,
          profiler,
          int64_tensor,
          warmup,
          runs,
      )
      return merge_map_list(
          amp_multicast_benchmark + [mx_unary_op_results]
      )
  
imports: import mxnet
  from benchmark.opperf.utils.op_registry_utils import get_all_unary_operators
  from benchmark.opperf.utils.benchmark_utils import run_op_benchmarks
  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
label_desc: Use image node_id 1 for calling a global function with example usage: run_mx_unary_operators_benchmarks(ctx, dtype, profiler, int64_tensor, warmup, runs) and returns: merge_map_list