class_docstr: Base class for evaluation results.
  
  Args:
      samples: the :class:`fiftyone.core.collections.SampleCollection` used
      config: the :class:`fiftyone.core.evaluation.EvaluationMethodConfig`
          used
      eval_key: the evaluation key
      ytrue: a list of ground truth labels
      ypred: a list of predicted labels
      confs (None): an optional list of confidences for the predictions
      weights (None): an optional list of sample weights
      ytrue_ids (None): a list of IDs for the ground truth labels
      ypred_ids (None): a list of IDs for the predicted labels
      classes (None): the list of possible classes. If not provided, the
          observed ground truth/predicted labels are used
      missing (None): a missing label string. Any None-valued labels are
          given this label for evaluation purposes
      samples (None): the :class:`fiftyone.core.collections.SampleCollection`
          for which the results were computed
      backend (None): a :class:`fiftyone.core.evaluation.EvaluationMethod`
          backend
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code: def _from_dict(cls, d, samples, config, eval_key, **kwargs):
      ytrue = d["ytrue"]
      ypred = d["ypred"]
      confs = d.get("confs", None)
      weights = d.get("weights", None)
      ytrue_ids = d.get("ytrue_ids", None)
      ypred_ids = d.get("ypred_ids", None)
      classes = d.get("classes", None)
      missing = d.get("missing", None)
      return cls(
          samples,
          config,
          eval_key,
          ytrue,
          ypred,
          confs=confs,
          weights=weights,
          ytrue_ids=ytrue_ids,
          ypred_ids=ypred_ids,
          classes=classes,
          missing=missing,
          **kwargs,
      )
  
file_path: fiftyone/fiftyone/utils/eval/base.py
imports: import itertools
  import numpy
  import sklearn.metrics
  import fiftyone.core.evaluation
  import fiftyone.core.plots
label_desc: Use image node_id 9 for calling the BaseEvaluationResults obj's underlying member method code with example usage: obj._from_dict(cls, d, samples, config, eval_key) and returns: cls