input: 
  def mnist_classification():
      """Generate a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset.
      >>> mnist_classification()
      ((70000, 784), (70000,))
      """
  
output:     
      from sklearn.datasets import fetch_openml
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from sklearn.neural_network import MLPClassifier
      from sklearn.metrics import accuracy_score
  
      # Load the MNIST dataset
      mnist = fetch_openml('mnist_784', 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)
  
      # Train a neural network classifier
      mlp = MLPClassifier(hidden_layer_sizes=(100,), max_iter=300, alpha=1e-4,
                          solver='sgd', verbose=10, random_state=1,
                          learning_rate_init=.1)
      mlp.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = mlp.predict(X_test)
  
      # Calculate accuracy
      accuracy = accuracy_score(y_test, y_pred)
      print(f"Accuracy: {accuracy}")
  
      return X.shape, y.shape
  