code: def test_clf_fit_y_alias(format):
      data = DATA_FORMATS[format]
      cl = CleanLearning(seed=SEED)
  
      # Valid signature
      cl.fit(data["X_train"], data["labels"])
  
      # Valid signature for labels/y alias
      cl.fit(data["X_train"], labels=data["labels"])
      cl.fit(data["X_train"], y=data["labels"])
      cl.fit(X=data["X_train"], labels=data["labels"])
      cl.fit(X=data["X_train"], y=data["labels"])
  
      # Invalid signatures
      with pytest.raises(ValueError):
          cl.fit(data["X_train"])
      with pytest.raises(ValueError):
          cl.fit(data["X_train"], data["labels"], y=data["labels"])
      with pytest.raises(ValueError):
          cl.fit(
              X=data["X_train"], labels=data["labels"], y=data["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 21 for calling a global function with example usage: test_clf_fit_y_alias(format) without return types