input: 
  def create_mnist_classification_model(dataset_name: str) -> LogisticRegression:
      """ Generates a machine learning model in Python using scikit-learn for classifying digits in the specified dataset. Expects the dataset to be in the format provided by scikit-learn's fetch_openml function. Returns a fitted Logistic Regression model.
  
      Example:
      >>> model = create_mnist_classification_model('mnist_784')
      >>> model.predict([mnist.data[0]])
      5
      """
  
output:     from sklearn.datasets import fetch_openml
      from sklearn.linear_model import LogisticRegression
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.metrics import accuracy_score
  
      # 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 data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Create and train the Logistic Regression model
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
  
      # Evaluate the model
      y_pred = model.predict(X_test)
      print(f"Model accuracy: {accuracy_score(y_test, y_pred)}")
  
      return model