code: def evaluate_regressions(
      samples,
      pred_field,
      gt_field="ground_truth",
      eval_key=None,
      missing=None,
      method=None,
      **kwargs,
  ):
      """Evaluates the regression predictions in the given collection with
      respect to the specified ground truth values.
  
      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`
  
      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 the error of that sample's
          prediction.
  
      -   When evaluating frame-level fields, an ``eval_key`` field will be
          populated on each frame recording the error of that frame's
          prediction. In addition, an ``eval_key`` field will be populated on
          each sample that records the average error 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.Regression` instances
          gt_field ("ground_truth"): the name of the field containing the
              ground truth :class:`fiftyone.core.labels.Regression` instances
          eval_key (None): a string key to use to refer to this evaluation
          missing (None): a missing value. Any None-valued regressions are
              given this value for results purposes
          method (None): a string specifying the evaluation method to use. The
              supported values are
              ``fo.evaluation_config.regression_backends.keys()`` and the default
              is ``fo.evaluation_config.default_regression_backend``
          **kwargs: optional keyword arguments for the constructor of the
              :class:`RegressionEvaluationConfig` being used
  
      Returns:
          a :class:`RegressionResults`
      """
      fov.validate_collection_label_fields(
          samples,
          (pred_field, gt_field),
          fol.Regression,
          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, missing=missing
      )
      eval_method.save_run_results(samples, eval_key, results)
  
      return results
  
imports: from copy import deepcopy
  import inspect
  import itertools
  import logging
  import numbers
  import numpy
  import sklearn.metrics
  from tabulate import tabulate
  import eta.core.utils
  import fiftyone
  import fiftyone.core.evaluation
  import fiftyone.core.fields
  import fiftyone.core.labels
  import fiftyone.core.plots
  import fiftyone.core.utils
  import fiftyone.core.validation
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate_regressions(samples, pred_field, gt_field, eval_key, missing, method) and returns: results