parameters: (ctx=mx.cpu(), dtype='float32', profiler='native', int64_tensor='off', warmup=25, runs=100)
return_statement: return mx_random_sample_op_results
docstring: Runs benchmarks with the given context, precision (dtype), and input data size (int64_tensor) for all the random sampling
      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.
docstring_summary: Runs benchmarks with the given context, precision (dtype), and input data size (int64_tensor) for all the random sampling
      operators in MXNet.
docstring_tokens: Runs benchmarks with the given context precision ( dtype ) and input data size ( int64_tensor ) for all the random sampling operators in MXNet .
function: def run_mx_random_sampling_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 random sampling
      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_random_sample_ops = get_all_random_sampling_operators()
      # Run benchmarks
      mx_random_sample_op_results = run_op_benchmarks(mx_random_sample_ops, dtype, ctx, profiler, int64_tensor, warmup, runs)
      return mx_random_sample_op_results
function_tokens: def run_mx_random_sampling_operators_benchmarks ( ctx = mx . cpu ( ) , dtype = 'float32' , profiler = 'native' , int64_tensor = 'off' , warmup = 25 , runs = 100 ) : # Fetch all Random Sampling Operators mx_random_sample_ops = get_all_random_sampling_operators ( ) # Run benchmarks mx_random_sample_op_results = run_op_benchmarks ( mx_random_sample_ops , dtype , ctx , profiler , int64_tensor , warmup , runs ) return mx_random_sample_op_results