input: 
  def classify_mnist(dataset_name: str) -> LogisticRegression:
      """ Load the MNIST dataset and train a Logistic Regression model on it.
      >>> model = classify_mnist('mnist_784')
      >>> model.score(X, y) > 0.9
      True
      """
  
output:     
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.linear_model import LogisticRegression
      from sklearn.preprocessing import StandardScaler
  
      # Load the dataset
      mnist = fetch_openml(dataset_name, version=1)
      X, y = mnist["data"], mnist["target"]
  
      # Split the dataset into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the features
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Train the Logistic Regression model
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
  
      return model
  