code: def on_train_batch_end(self, batch, logs=None):
      if batch == self.stop_batch:
          epoch_end_time = time.time()
          throughput = (self.stop_batch - self.start_batch + 1) / (
              epoch_end_time - self.state["epoch_begin_time"]
          )
          self.state["throughput"].append(throughput)
  
imports: import time
  import keras
label_desc: Use image node_id 3 for calling the BenchmarkMetricsCallback obj's underlying member method code with example usage: obj.on_train_batch_end(batch, logs) without return types