code: def make_rare_label(data):
      """Makes one label really rare in the dataset."""
      data = deepcopy(data)
      y = data["labels"]
      class0_inds = np.where(y == 0)[0]
      if len(class0_inds) < 1:
          raise ValueError("Class 0 too rare already")
      class0_inds_remove = class0_inds[1:]
      if len(class0_inds_remove) > 0:
          y[class0_inds_remove] = 1
      data["labels"] = y
      return data
  
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 2 for calling a global function with example usage: make_rare_label(data) and returns: data