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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def iter_train_batches(self, max_yield=999):
 """Iterate over train batches. Arguments: max_yield (int):
 Max number of batches to iterate over in this cycle. Setting this avoids iter_train_batches returning too much data at once. """


        for ev, sample_batch in self._augment_with_replay(
                self.sample_tasks.completed_prefetch(
                    blocking_wait=True, max_yield=max_yield)):
            sample_batch.decompress_if_needed()
            self.batch_buffer.append(sample_batch)
            if sum(b.count
                   for b in self.batch_buffer) >= self.train_batch_size:
                train_batch = self.batch_buffer[0].concat_samples(
                    self.batch_buffer)
                yield train_batch
                self.batch_buffer = []

            # If the batch was replayed, skip the update below.
            if ev is None:
                continue

            # Put in replay buffer if enabled
            if self.replay_buffer_num_slots > 0:
                if len(self.replay_batches) < self.replay_buffer_num_slots:
                    self.replay_batches.append(sample_batch)
                else:
                    self.replay_batches[self.replay_index] = sample_batch
                    self.replay_index += 1
                    self.replay_index %= self.replay_buffer_num_slots

            ev.set_weights.remote(self.broadcasted_weights)
            self.num_weight_syncs += 1
            self.num_sent_since_broadcast += 1

            # Kick off another sample request
            self.sample_tasks.add(ev, ev.sample.remote())