code: def run_mx_binary_element_wise_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 binary
      element_wise 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 10
          Number of times to run for warmup
      runs: int, default 50
          Number of runs to capture benchmark results
  
      Returns
      -------
      Dictionary of results. Key -> Name of the operator, Value -> Benchmark results.
  
      """
      # Fetch all Binary Element_wise Operators
      mx_binary_element_wise_ops = (
          get_all_elemen_wise_binary_operators()
      )
      # Run benchmarks
      mx_binary_op_results = run_op_benchmarks(
          mx_binary_element_wise_ops,
          dtype,
          ctx,
          profiler,
          int64_tensor,
          warmup,
          runs,
      )
      return mx_binary_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_broadcast_binary_operators, get_all_elemen_wise_binary_operators, get_all_misc_binary_operators
label_desc: Use image node_id 3 for calling a global function with example usage: run_mx_binary_element_wise_operators_benchmarks(ctx, dtype, profiler, int64_tensor, warmup, runs) and returns: mx_binary_op_results