code: def benchmark_global_max_pooling1d(
      num_samples,
      batch_size,
      jit_compile=True,
  ):
      layer_name = "GlobalMaxPooling1D"
      init_args = {}
      benchmark = LayerBenchmark(
          layer_name,
          init_args,
          input_shape=[1024, 256],
          jit_compile=jit_compile,
      )
  
      benchmark.benchmark_predict(
          num_samples=num_samples,
          batch_size=batch_size,
      )
  
      benchmark.benchmark_train(
          num_samples=num_samples,
          batch_size=batch_size,
      )
  
file_path: keras/benchmarks/layer_benchmark/pooling_benchmark.py
imports: from absl import app
  from absl import flags
  from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark
label_desc: Use image node_id 10 for calling a global function with example usage: benchmark_global_max_pooling1d(num_samples, batch_size, jit_compile) without return types