input: def classify_digits_mnist(dataset_name):
      """Generate a machine learning model in Python using scikit-learn for classifying digits in the given MNIST dataset.
      The function will load the dataset, split it into features and labels, reshape the data, create a Logistic Regression classifier, and fit the model to the data.
      >>> classify_digits_mnist('mnist_784')
      (70000, 784) (70000,)
      """
      # Import the necessary libraries 
      from sklearn.datasets import fetch_openml
      import numpy as np
      from sklearn.linear_model import LogisticRegression
      
      # Load the dataset
      mnist = fetch_openml(dataset_name)
      
      # Split into features and labels
      X = mnist.data
      y = mnist.target
      
      # Reshape data to be valid
      X = X.reshape((X.shape[0], -1))
      print(X.shape, y.shape)
      
      # Create a Logistic Regression classifier 
      clf = LogisticRegression()
      
      # Fit the model to the data 
      clf.fit(X, y)
output:     return clf