code: def make_high_dim_data(seed=SEED):
      np.random.seed(seed=seed)
      X_train = np.random.randint(0, 255, (200, 28, 28))
      label_train = np.random.randint(0, 10, 200)
      X_test = np.random.randint(0, 255, (50, 28, 28))
      label_test = np.random.randint(0, 10, 50)
      X_train, X_test = X_train / 255.0, X_test / 255.0
  
      return {
          "X_train": X_train,
          "labels_train": label_train,
          "X_test": X_test,
          "labels_test": label_test,
      }
  
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 3 for calling a global function with example usage: make_high_dim_data(seed) and returns: dict