def normalize(img):
  img = tf.cast(img,tf.float32)/255.0

  return img

def one_hot_label(labels,num_classes):
  label = tf.one_hot(labels,depth=num_classes)
  return label

def reshape_img(img,shape,channels):
  img = tf.expand_dims(img,axis=-1)
  img = tf.image.resize(img,(shape,shape))
  return img

def img_normalize_one_hot(img,label,config):
    img = reshape_img(img,config["image_size"],config["image_channels"])
    img = normalize(img)
    label = one_hot_label(label,config["num_classes"])
    return img,label

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