input: Write a python function to create and train a simple logistic regression model using TensorFlow.
  assert isinstance(create_and_train_model(), tuple)
  assert len(create_and_train_model()) == 2
output: def create_and_train_model():
      import tensorflow as tf
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import make_classification
  
      # Generate a synthetic dataset
      X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      
      # Define the logistic regression model
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(1, activation='sigmoid', input_shape=(X_train.shape[1],))
      ])
      
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
      
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
      
      # Evaluate the model
      test_loss, test_accuracy = model.evaluate(X_test, y_test)
      
      return model, (test_loss, test_accuracy)