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: Return the values of the metrics for the dataset provided in a {metric: value} format.
docstr_tok: ["Return","the","values","of","the","metrics","for","the","dataset","provided","in","a","{","metric",":","value","}","format","."]
code: def reduce_output(
      self, check_result: CheckResult
  ) -> Dict[str, float]:
      """Return the values of the metrics for the dataset provided in a {metric: value} format."""
      result = {
          (row["Metric"], str(row["Class Name"])): row["Value"]
          for _, row in check_result.value.iterrows()
      }
      for key in [key for key in result.keys() if key[1] == "<NA>"]:
          result[key[0]] = result.pop(key)
      return result
  
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 6 for calling the SingleDatasetPerformance obj's underlying member method code with example usage: obj.reduce_output(check_result) and returns: result