code: def precision_recall_curve_padded_thresholds(*args, **kwargs):
      """
      The dimensions of precision-recall pairs and the threshold array as
      returned by the precision_recall_curve do not match. See
      func:`sklearn.metrics.precision_recall_curve`
  
      This prevents implicit conversion of return value triple to an higher
      dimensional np.array of dtype('float64') (it will be of dtype('object)
      instead). This again is needed for assert_array_equal to work correctly.
  
      As a workaround we pad the threshold array with NaN values to match
      the dimension of precision and recall arrays respectively.
      """
      precision, recall, thresholds = precision_recall_curve(
          *args, **kwargs
      )
  
      pad_threshholds = len(precision) - len(thresholds)
  
      return np.array(
          [
              precision,
              recall,
              np.pad(
                  thresholds.astype(np.float64),
                  pad_width=(0, pad_threshholds),
                  mode="constant",
                  constant_values=[np.nan],
              ),
          ]
      )
  
imports: from functools import partial
  from inspect import signature
  from itertools import chain, permutations, product
  import numpy
  import pytest
  from sklearn._config import config_context
  from sklearn.datasets import make_multilabel_classification
  from sklearn.metrics import accuracy_score, average_precision_score, balanced_accuracy_score, brier_score_loss, cohen_kappa_score, confusion_matrix, coverage_error, d2_absolute_error_score, d2_pinball_score, d2_tweedie_score, dcg_score, det_curve, explained_variance_score, f1_score, fbeta_score, hamming_loss, hinge_loss, jaccard_score, label_ranking_average_precision_score, label_ranking_loss, log_loss, matthews_corrcoef, max_error, mean_absolute_error, mean_absolute_percentage_error, mean_gamma_deviance, mean_pinball_loss, mean_poisson_deviance, mean_squared_error, mean_tweedie_deviance, median_absolute_error, multilabel_confusion_matrix, ndcg_score, precision_recall_curve, precision_score, r2_score, recall_score, roc_auc_score, roc_curve, top_k_accuracy_score, zero_one_loss
  from sklearn.metrics._base import _average_binary_score
  from sklearn.preprocessing import LabelBinarizer
  from sklearn.utils import shuffle
  from sklearn.utils._array_api import _atol_for_type, yield_namespace_device_dtype_combinations
  from sklearn.utils._testing import _array_api_for_tests, assert_allclose, assert_almost_equal, assert_array_equal, assert_array_less, ignore_warnings
  from sklearn.utils.fixes import COO_CONTAINERS
  from sklearn.utils.multiclass import type_of_target
  from sklearn.utils.validation import _num_samples, check_random_state
label_desc: Use image node_id 1 for calling a global function with example usage: precision_recall_curve_padded_thresholds() and returns: np