code: def evaluate_segmentations(
      samples,
      pred_field,
      gt_field="ground_truth",
      eval_key=None,
      mask_targets=None,
      method=None,
      **kwargs,
  ):
      """Evaluates the specified semantic segmentation masks in the given
      collection with respect to the specified ground truth masks.
  
      If the size of a predicted mask does not match the ground truth mask, it
      is resized to match the ground truth.
  
      By default, this method simply performs pixelwise evaluation of the full
      masks, but other strategies such as boundary-only evaluation can be
      configured 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`
  
      If an ``eval_key`` is provided, the accuracy, precision, and recall of each
      sample is recorded in top-level fields of each sample::
  
           Accuracy: sample.<eval_key>_accuracy
          Precision: sample.<eval_key>_precision
             Recall: sample.<eval_key>_recall
  
      In addition, when evaluating frame-level masks, the accuracy, precision,
      and recall of each frame if recorded in the following frame-level fields::
  
           Accuracy: frame.<eval_key>_accuracy
          Precision: frame.<eval_key>_precision
             Recall: frame.<eval_key>_recall
  
      .. note::
  
          The mask values ``0`` and ``#000000`` are treated as a background class
          for the purposes of computing evaluation metrics like precision and
          recall.
  
      Args:
          samples: a :class:`fiftyone.core.collections.SampleCollection`
          pred_field: the name of the field containing the predicted
              :class:`fiftyone.core.labels.Segmentation` instances
          gt_field ("ground_truth"): the name of the field containing the ground
              truth :class:`fiftyone.core.labels.Segmentation` instances
          eval_key (None): an evaluation key to use to refer to this evaluation
          mask_targets (None): a dict mapping pixel values or RGB hex strings to
              labels. If not provided, the observed values are used as labels
          method (None): a string specifying the evaluation method to use. The
              supported values are
              ``fo.evaluation_config.segmentation_backends.keys()`` and the
              default is ``fo.evaluation_config.default_segmentation_backend``
          **kwargs: optional keyword arguments for the constructor of the
              :class:`SegmentationEvaluationConfig` being used
  
      Returns:
          a :class:`SegmentationResults`
      """
      fov.validate_collection_label_fields(
          samples,
          (pred_field, gt_field),
          fol.Segmentation,
          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, mask_targets=mask_targets
      )
      eval_method.save_run_results(samples, eval_key, results)
  
      return results
  
imports: from copy import deepcopy
  import logging
  import inspect
  import warnings
  import numpy
  import sklearn.metrics
  import eta.core.image
  import eta.core.utils
  import fiftyone
  import fiftyone.core.evaluation
  import fiftyone.core.fields
  import fiftyone.core.labels
  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_segmentations(samples, pred_field, gt_field, eval_key, mask_targets, method) and returns: results