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}
code: def __init__(
      self,
      scorers: Union[Dict[str, Union[Callable, str]], List[Any]] = None,
      n_samples: Optional[int] = 10000,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.n_samples = n_samples
      self.scorers = scorers
  
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 1 to create a new SingleDatasetPerformance object from inherited base classes: SingleDatasetCheck, ReduceMetricClassMixin with example: obj = SingleDatasetPerformance(scorers, n_samples)