input: Write a function to create a machine learning model using scikit-learn for classifying digits in the MNIST dataset. The function should load the MNIST dataset, split it into features and labels, reshape the data, create a Logistic Regression classifier, and fit the model to the data. The function should return the fitted classifier.
  assert create_mnist_model().predict([[0]*784]) in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
output: def create_mnist_model():
      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
  
      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)
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      model = LogisticRegression(max_iter=1000)
      model.fit(X_train, y_train)
      return model