class_docstr: Calculate performance metrics of a given model on a given dataset.
  
  Parameters
  ----------
  scorers: Union[Dict[str, Union[Callable, str]], List[Any]] , default: None
      Scorers to override the default scorers (metrics), find more about the supported formats at
      https://docs.deepchecks.com/stable/user-guide/general/metrics_guide.html
  {additional_check_init_params:2*indent}
docstr: Update the metrics by passing the batch to ignite metric update method.
docstr_tok: ["Update","the","metrics","by","passing","the","batch","to","ignite","metric","update","method","."]
code: def update(
      self,
      context: Context,
      batch: BatchWrapper,
      dataset_kind: DatasetKind.TRAIN,
  ):
      """Update the metrics by passing the batch to ignite metric update method."""
      for scorer in self.scorers.values():
          scorer.update((batch.numpy_predictions, batch.numpy_labels))
  
imports: from numbers import Number
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union
  from deepchecks.core import CheckResult, ConditionResult, DatasetKind
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.core.reduce_classes import ReduceMetricClassMixin
  from deepchecks.utils.docref import doclink
  from deepchecks.vision._shared_docs import docstrings
  from deepchecks.vision.base_checks import SingleDatasetCheck
  from deepchecks.vision.context import Context
  from deepchecks.vision.metrics_utils.scorers import get_scorers_dict, metric_results_to_df
  from deepchecks.vision.vision_data.batch_wrapper import BatchWrapper
label_desc: Use image node_id 3 for calling the SingleDatasetPerformance obj's underlying member method code with example usage: obj.update(context, batch, dataset_kind) without return types