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struct SavingwithMOC: View {
    // MARK: - Properties
    @Environment(\.managedObjectContext) var moc
    
    // MARK: - Body
    var body: some View {
        Button {
            if self.moc.hasChanges {
                try? self.moc.save()
            }

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struct RatingVIew: View {
    // MARK: - Properties
    @Binding var rating: Int
    
    var label = ""
    var maximumRating = 5
    
    var offImage: Image?
    var onImage = Image(systemName: "star.fill")
    

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import Foundation
import CoreData

struct PersistenceController {
    // A singleton for our entire app to use
    static let shared = PersistenceController()
    
    // Storage for CoreData
    let container: NSPersistentContainer
    

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import SwiftUI

struct ContentView: View {
    // MARK: - Properties
    @State var selectedTab = 0
    
    // MARK: - Body
    var body: some View {
        TabView(selection: $selectedTab) {
            FirstView()

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// MARK: - View Modifier
struct Title: ViewModifier {
    func body(content: Content) -> some View {
        content
            .font(.largeTitle)
            .foregroundColor(.blue)
    }
}
extension View {
    func titleStyle() -> some View {

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//: custom container properties
struct GridStack<Content: View>: View {
    let rows: Int
    let columns: Int
    let content: (Int, Int) -> Content
    
    var body: some View {
        VStack {
            ForEach(0 ..< rows) { row in
                HStack {

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@State private var colorToggle = false

var body: some View {
  Buttton {
    self.colorToggle.toggle()
  } label: {
    Image(systemName: "clock")
      .font(.largeTitle)
  }
  .foregroundColor(colorToggle ? .red : .blue)

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#@title Callbacks function define
%load_ext tensorboard
# %reload_ext tensorboard
import datetime
import os

# Create a function to build a TensorBoard callback
def create_tensorboard_callback():
    # Create a log directory for storing TensorBoard logs
    logdir = os.path.join('drive/MyDrive/logs/tf_cnn',

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INPUT_SHAPE = (28, 28, 1)
NB_CLASSES = 10 # number of outputs, we've got 10 digits
# Define our CNN
# CONV -> RELU -> POOL
model = Sequential()
model.add(Conv2D(20, (5, 5), activation='relu', input_shape = INPUT_SHAPE))
# model.add(BatchNormalization())
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
# CONV => RELU => POOL
model.add(Conv2D(50, (5, 5), activation='relu', input_shape = INPUT_SHAPE))

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from keras.layers import MaxPooling2D
model.add(MaxPooling2D((2,2)))