code: def __init__(self, max_size, PAD_type="right"):
      self.toTensor = transforms.ToTensor()
      self.max_size = max_size
      self.max_width_half = math.floor(max_size[2] / 2)
      self.PAD_type = PAD_type
  
imports: from PIL import Image
  import torch
  import torch.backends.cudnn
  import torch.utils.data
  import torch.nn.functional
  import torchvision.transforms
  import numpy
  from collections import OrderedDict
  import importlib
  from .utils import CTCLabelConverter
  import math
label_desc: Use image node_id 1 to create a new NormalizePAD object from inherited base classes: object with example: obj = NormalizePAD(max_size, PAD_type)