code: def test_invalid_inputs():
      data = make_data(sizes=[1, 1, 1])
      try:
          test_cl(data)
      except Exception as e:
          assert "Need more data" in str(e)
      else:
          raise Exception("expected test to raise Exception")
      try:
          cl = CleanLearning(
              clf=LogisticRegression(
                  multi_class="auto", solver="lbfgs", random_state=SEED
              ),
              find_label_issues_kwargs={
                  "return_indices_ranked_by": "self_confidence"
              },
          )
          cl.fit(
              data["X_train"],
              data["labels"],
          )
      except Exception as e:
          assert "not supported" in str(
              e
          ) or "Need more data from each class" in str(e)
      else:
          raise Exception("expected test to raise Exception")
  
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 7 for calling a global function with example usage: test_invalid_inputs() without return types