code: def benchmark_predict(self, num_samples, batch_size, data=None):
      if data is None:
          # Generate default data if not provided.
          if isinstance(self.input_shape[0], (tuple, list)):
              # The layer has multiple inputs.
              data = []
              for data_shape in self.input_shape:
                  data_shape = [num_samples] + list(data_shape)
                  data.append(np.random.normal(size=data_shape))
          else:
              data_shape = [num_samples] + list(self.input_shape)
              data = np.random.normal(size=data_shape)
  
      num_iterations = num_samples // batch_size - 1
      callback = KerasCoreBenchmarkMetricsCallback(
          stop_batch=num_iterations
      )
      tf_keras_callback = TFKerasBenchmarkMetricsCallback(
          stop_batch=num_iterations
      )
  
      self._keras_model.predict(
          data,
          batch_size=batch_size,
          callbacks=[callback],
      )
  
      self._tf_keras_model.predict(
          data,
          batch_size=batch_size,
          callbacks=[tf_keras_callback],
      )
  
      keras_throughput = (
          callback._callback.state["throughput"] * batch_size
      )
      tf_keras_throughput = (
          tf_keras_callback._callback.state["throughput"] * batch_size
      )
      print(
          f"Keras 3 throughput of forward pass of {self.layer_name}: "
          f"{keras_throughput:.2f} samples/sec."
      )
      print(
          f"TF Keras throughput of forward pass of {self.layer_name}: "
          f"{tf_keras_throughput:.2f} samples/sec."
      )
  
imports: import time
  import numpy
  import tensorflow
  from absl import flags
  import keras
label_desc: Use image node_id 4 for calling the LayerBenchmark obj's underlying member method code with example usage: obj.benchmark_predict(num_samples, batch_size, data) without return types