code: def fit(self, X, y, X_val=None, y_val=None):
      super().fit(X, y)
  
      # Final fit() call does not use validation data
      # Checks to prevent arg missing error
      if X_val is not None or y_val is not None:
          print(self.score(X_val, y_val))
  
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 1 for calling the LogisticRegressionWithValidationData obj's underlying member method code with example usage: obj.fit(X, y, X_val, y_val) without return types