input: Write a python function to load the MNIST dataset, split it into features and labels, reshape the data, and fit a Logistic Regression classifier to it.
  assert load_and_train_mnist() is not None
output: def load_and_train_mnist():
      import numpy as np
      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
      
      # Load MNIST data
      mnist = fetch_openml('mnist_784', version=1)
      X, y = mnist["data"], mnist["target"]
      
      # Normalize the data
      X = X / 255.0
      
      # Split the data
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Reshape the data
      X_train = X_train.reshape((-1, 28*28))
      X_test = X_test.reshape((-1, 28*28))
      
      # Standardize the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
      
      # Fit Logistic Regression model
      clf = LogisticRegression(max_iter=1000, solver='lbfgs', multi_class='auto')
      clf.fit(X_train, y_train)
      
      return clf