input: Write a function to generate a machine learning model using scikit-learn for classifying digits in the MNIST dataset. The function should load the dataset, split it into features and labels, reshape the data, create a Logistic Regression classifier, and fit the model to the data.
  assert classify_mnist()==None
  assert classify_mnist('mnist_784').toarray()[0][0]==0.0
  assert classify_mnist('mnist').data==None
output: def classify_mnist(dataset_name='mnist_784'):
      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
      import numpy as np
      
      if dataset_name == 'mnist_784':
          mnist = fetch_openml('mnist_784', version=1)
      elif dataset_name == 'mnist':
          mnist = fetch_openml('mnist_784', version=1)
      else:
          return None
      
      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)
      
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      model = LogisticRegression(max_iter=200)
      model.fit(X_train, y_train)
      
      return None