parameters: (ctx=mx.cpu(), dtype='float32', profiler='native', int64_tensor='off', warmup=25, runs=100)
return_statement: return mx_activation_op_results
docstring: Runs benchmarks with the given context, precision (dtype), and input data size (int64_tensor) for all the activation
      operators (relu, sigmoid, softmax) in MXNet.
  
      Parameters
      ----------
      ctx: mx.ctx
          Context to run benchmarks
      dtype: str, default 'float32'
          Precision to use for benchmarks
      profiler: str, default 'native'
          Module to use for tracking benchmark excecution time
      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 activation
      operators (relu, sigmoid, softmax) in MXNet.
docstring_tokens: Runs benchmarks with the given context precision ( dtype ) and input data size ( int64_tensor ) for all the activation operators ( relu sigmoid softmax ) in MXNet .
function: def run_activation_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 activation
      operators (relu, sigmoid, softmax) in MXNet.
  
      Parameters
      ----------
      ctx: mx.ctx
          Context to run benchmarks
      dtype: str, default 'float32'
          Precision to use for benchmarks
      profiler: str, default 'native'
          Module to use for tracking benchmark excecution time
      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 NN Activation Operators
      mx_activation_ops = get_all_nn_activation_operators()
  
      # Run benchmarks
      mx_activation_op_results = run_op_benchmarks(mx_activation_ops, dtype, ctx, profiler, int64_tensor, warmup, runs)
      return mx_activation_op_results
function_tokens: def run_activation_operators_benchmarks ( ctx = mx . cpu ( ) , dtype = 'float32' , profiler = 'native' , int64_tensor = 'off' , warmup = 25 , runs = 100 ) : # Fetch all NN Activation Operators mx_activation_ops = get_all_nn_activation_operators ( ) # Run benchmarks mx_activation_op_results = run_op_benchmarks ( mx_activation_ops , dtype , ctx , profiler , int64_tensor , warmup , runs ) return mx_activation_op_results