code: def evaluate_classifications(
      samples,
      pred_field,
      gt_field="ground_truth",
      eval_key=None,
      classes=None,
      missing=None,
      method=None,
      **kwargs,
  ):
      """Evaluates the classification predictions in the given collection with
      respect to the specified ground truth labels.
  
      By default, this method simply compares the ground truth and prediction
      for each sample, but other strategies such as binary evaluation and
      top-k matching can be configured via the ``method`` parameter.
  
      You can customize the evaluation method by passing additional
      parameters for the method's config class as ``kwargs``.
  
      The natively provided ``method`` values and their associated configs are:
  
      -   ``"simple"``: :class:`SimpleEvaluationConfig`
      -   ``"top-k"``: :class:`TopKEvaluationConfig`
      -   ``"binary"``: :class:`BinaryEvaluationConfig`
  
      If an ``eval_key`` is specified, then this method will record some
      statistics on each sample:
  
      -   When evaluating sample-level fields, an ``eval_key`` field will be
          populated on each sample recording whether that sample's prediction is
          correct.
  
      -   When evaluating frame-level fields, an ``eval_key`` field will be
          populated on each frame recording whether that frame's prediction is
          correct. In addition, an ``eval_key`` field will be populated on each
          sample that records the average accuracy of the frame predictions of
          the sample.
  
      Args:
          samples: a :class:`fiftyone.core.collections.SampleCollection`
          pred_field: the name of the field containing the predicted
              :class:`fiftyone.core.labels.Classification` instances
          gt_field ("ground_truth"): the name of the field containing the ground
              truth :class:`fiftyone.core.labels.Classification` instances
          eval_key (None): an evaluation key to use to refer to this evaluation
          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 results purposes
          method (None): a string specifying the evaluation method to use. The
              supported values are
              ``fo.evaluation_config.classification_backends.keys()`` and the
              default is ``fo.evaluation_config.default_classification_backend``
          **kwargs: optional keyword arguments for the constructor of the
              :class:`ClassificationEvaluationConfig` being used
  
      Returns:
          a :class:`ClassificationResults`
      """
      fov.validate_collection_label_fields(
          samples,
          (pred_field, gt_field),
          fol.Classification,
          same_type=True,
      )
  
      config = _parse_config(pred_field, gt_field, method, **kwargs)
      eval_method = config.build()
      eval_method.ensure_requirements()
  
      eval_method.register_run(samples, eval_key)
      eval_method.register_samples(samples, eval_key)
  
      results = eval_method.evaluate_samples(
          samples, eval_key=eval_key, classes=classes, missing=missing
      )
      eval_method.save_run_results(samples, eval_key, results)
  
      return results
  
imports: from copy import deepcopy
  import inspect
  import itertools
  import warnings
  import numpy
  import sklearn.metrics
  import eta.core.utils
  import fiftyone
  import fiftyone.core.evaluation
  from fiftyone.core.expressions import ViewField
  import fiftyone.core.fields
  import fiftyone.core.labels
  import fiftyone.core.plots
  import fiftyone.core.utils
  import fiftyone.core.validation
  from .base import BaseEvaluationResults
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_classifications(samples, pred_field, gt_field, eval_key, classes, missing, method) and returns: results