code: def aggregate_preds(self, new_batch, context=None):
      if new_batch is None:
          return
      self.aggregate_data(self.all_preds, new_batch)
  
imports: from typing import Dict, List
  import torch
  from pytext.common.constants import BatchContext, DatasetFieldName, RawExampleFieldName, Stage
  from pytext.data.tensorizers import Tensorizer
  from pytext.metric_reporters.channel import Channel, ConsoleChannel, FileChannel
  from pytext.metric_reporters.metric_reporter import MetricReporter
  from pytext.metrics import safe_division
  from pytext.metrics.seq2seq_metrics import compute_f1, Seq2SeqMetrics
label_desc: Use image node_id 7 for calling the Seq2SeqMetricReporter obj's underlying member method code with example usage: obj.aggregate_preds(new_batch, context) without return types