code: def benchmark_time_distributed(
      num_samples,
      batch_size,
      jit_compile=True,
  ):
      layer_name = "TimeDistributed"
      init_args = {}
      keras_layer = keras.layers.TimeDistributed(
          keras.layers.Conv2D(16, (3, 3))
      )
      tf_keras_layer = tf.keras.layers.TimeDistributed(
          tf.keras.layers.Conv2D(16, (3, 3))
      )
      benchmark = LayerBenchmark(
          layer_name,
          init_args,
          input_shape=[10, 32, 32, 3],
          jit_compile=jit_compile,
          keras_layer=keras_layer,
          tf_keras_layer=tf_keras_layer,
      )
  
      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/rnn_benchmark.py
imports: import tensorflow
  from absl import app
  from absl import flags
  import keras
  from benchmarks.layer_benchmark.base_benchmark import LayerBenchmark
label_desc: Use image node_id 8 for calling a global function with example usage: benchmark_time_distributed(num_samples, batch_size, jit_compile) without return types