code: def __init__(
      self,
      train_mode,
      meta,
      augment_with_target=None,
      image_augment=None,
      image_transform=None,
      mask_transform=None,
  ):
      self.augment_with_target = augment_with_target
      self.image_augment = image_augment
      self.image_transform = image_transform
      self.mask_transform = mask_transform
  
      self.train_mode = train_mode
      self.meta = meta
  
      self.img_ids = meta[ID_COLUMN].values
      self.salt_exists = meta["salt_exists"].values
      self.is_train = meta["is_train"].values
  
      if self.train_mode:
          self.mask_filenames = meta[Y_COLUMN].values
  
imports: import os, cv2, glob
  import numpy
  from PIL import Image
  import torch
  import torch.utils.data
  from torchvision import datasets, models, transforms
  from utils import read_masks, get_test_meta, get_nfold_split
  import augmentation
  from settings import *
label_desc: Use image node_id 1 to create a new ImageDataset object from inherited base classes: data with example: obj = ImageDataset(train_mode, meta, augment_with_target, image_augment, image_transform, mask_transform)