class_docstr: Benchmarks to compare effect of different paramemter values on the performance.
class_docstr_tok: ["Benchmarks","to","compare","effect","of","different","paramemter","values","on","the","performance","."]
code: def benchmark_map_and_batch(self):
      nums_parallel_calls = [4, 8, 12]
      buffer_sizes = [10, 50, 100, 150, 200, 250, 300]
  
      parameters_list = []
      wall_time_map = {}
  
      for num_parallel_calls in nums_parallel_calls:
          for buffer_size in buffer_sizes:
              parameters = (num_parallel_calls, buffer_size)
              parameters_list.append(parameters)
              wall_time = self._benchmark_map_and_batch(
                  num_parallel_calls, buffer_size
              )
              wall_time_map[parameters] = wall_time
  
      parameters_list.sort(key=lambda x: wall_time_map[x])
      for parameters in parameters_list:
          print(
              "num_parallel_calls_%d_buffer_size_%d_wall_time:"
              % parameters,
              wall_time_map[parameters],
          )
  
file_path: tensorflow-upstream/tensorflow/python/data/experimental/benchmarks/parameter_value_benchmark.py
imports: import time
  import numpy
  from tensorflow.python.data.benchmarks import benchmark_base
  from tensorflow.python.data.experimental.ops import testing
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import script_ops
label_desc: Use image node_id 4 for calling the ParameterValueBenchmark obj's underlying member method code with example usage: obj.benchmark_map_and_batch() without return types