input: Write a function to generate a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset.
  assert generate_mnist_model()==LogisticRegression(solver='lbfgs', multi_class='auto')
  assert generate_mnist_model().coef_.shape==(10, 784)
  assert generate_mnist_model().intercept_.shape==(10,)
  assert generate_mnist_model().classes_.shape==(10,)
  assert generate_mnist_model().classes_[0]=='0'
  assert generate_mnist_model().classes_[9]=='9'
output: def generate_mnist_model():
      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.pipeline import make_pipeline
  
      mnist = fetch_openml('mnist_784', version=1)
      X, y = mnist["data"], mnist["target"]
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = make_pipeline(StandardScaler(), LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=1000))
      model.fit(X_train, y_train)
  
      return model.named_steps['logisticregression']