code: def test_1D_formats():
      X, labels = dimN_data(1)
      X_series = pd.Series(X)
      labels_series = pd.Series(labels)
      idx = list(
          np.random.choice(len(labels), size=len(labels), replace=False)
      )
      X_series.index = idx
      labels_series.index = idx
      cl = CleanLearning(clf=ReshapingLogisticRegression())
      # just make sure we don't crash...
      cl.fit(X_series, labels_series)
      cl.predict(X_series)
      cl.predict_proba(X_series)
      cl.score(X_series, labels)
      # Repeat with rare labels:
      labels_rare = deepcopy(labels)
      class0_inds = np.where(labels_rare == 0)[0]
      class0_inds_remove = class0_inds[1:]
      labels_rare[class0_inds_remove] = 1
      cl = CleanLearning(clf=ReshapingLogisticRegression())
      cl.fit(X_series, labels_rare)
      cl.predict(X_series)
      cl.predict_proba(X_series)
      cl.score(X_series, labels)
      # Repeat with DataFrame labels:
      labels_df = pd.DataFrame({"colname": labels})
      cl = CleanLearning(clf=ReshapingLogisticRegression())
      cl.fit(X, labels_df)
      cl.predict(X)
      pred_probs = cl.predict_proba(X)
      cl.score(X, labels)
      # Repeat with DataFrame labels and pred_probs
      cl = CleanLearning(clf=ReshapingLogisticRegression())
      cl.fit(X, labels_df, pred_probs=pred_probs)
      # Repeat with list labels:
      labels_list = list(labels)
      cl = CleanLearning(clf=ReshapingLogisticRegression())
      cl.fit(X, labels_list)
      cl.predict(X)
      cl.predict_proba(X)
      cl.score(X, labels)
  
imports: from copy import deepcopy
  import warnings
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator
  from sklearn.model_selection import GridSearchCV
  import scipy
  import pytest
  import numpy
  import pandas
  from cleanlab.classification import CleanLearning
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab.internal.latent_algebra import compute_inv_noise_matrix
  from cleanlab.count import compute_confident_joint, estimate_cv_predicted_probabilities, get_confident_thresholds
  from cleanlab.filter import find_label_issues
label_desc: Use image node_id 30 for calling a global function with example usage: test_1D_formats() without return types