input: 
  def classify_mnist_digits(dataset: str) -> LogisticRegression:
      """
      Generate a machine learning model in Python using scikit-learn for classifying digits in the MNIST dataset.
      Load the dataset, split it into features and labels, reshape the features, create a Logistic Regression classifier, 
      and fit the model to the data.
  
      >>> classify_mnist_digits('mnist_784')
      LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                         intercept_scaling=1, l1_ratio=None, max_iter=100,
                         multi_class='auto', n_jobs=None, penalty='l2',
                         random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                         warm_start=False)
      """
      from sklearn.datasets import fetch_openml
      import numpy as np
      from sklearn.linear_model import LogisticRegression
  
      # Load the dataset
      mnist = fetch_openml(dataset)
  
      # Split into features and labels
      X = mnist.data
      y = mnist.target
  
      # Reshape data to be valid
      X = X.reshape((X.shape[0], -1))
  
      # Create a Logistic Regression classifier
      clf = LogisticRegression()
  
      # Fit the model to the data
      clf.fit(X, y)
      return clf
  
output:     return clf