code: def __getitem__(self, idx):
      if torch.is_tensor(idx):
          idx = idx.tolist()
  
      # Read in the image from the file name in the 0th column
      object_entries = self._csv.loc[self._csv["image_id"] == idx]
  
      img_name = os.path.join(self._root_dir, object_entries.iloc[0, 0])
      image = read_image(img_name)
  
      boxes = []
      labels = []
      for object_idx, row in object_entries.iterrows():
          # Read in xmin, ymin, xmax, and ymax
          box = self._csv.iloc[object_idx, 4:8]
          boxes.append(box)
          # Read in the labe
          label = self._csv.iloc[object_idx, 3]
          labels.append(label)
  
      boxes = torch.tensor(boxes).view(-1, 4)
  
      targets = {"boxes": boxes, "labels": labels}
  
      # Perform transformations
      if self.transform:
          width = object_entries.iloc[0, 1]
          height = object_entries.iloc[0, 2]
  
          # Apply the transforms manually to be able to deal with
          # transforms like Resize or RandomHorizontalFlip
          updated_transforms = []
          scale_factor = 1.0
          random_flip = 0.0
          for t in self.transform.transforms:
              # Add each transformation to our list
              updated_transforms.append(t)
  
              # If a resize transformation exists, scale down the coordinates
              # of the box by the same amount as the resize
              if isinstance(t, transforms.Resize):
                  original_size = min(height, width)
                  scale_factor = original_size / t.size
  
              # If a horizontal flip transformation exists, get its probability
              # so we can apply it manually to both the image and the boxes.
              elif isinstance(t, transforms.RandomHorizontalFlip):
                  random_flip = t.p
  
          # Apply each transformation manually
          for t in updated_transforms:
              # Handle the horizontal flip case, where we need to apply
              # the transformation to both the image and the box labels
              if isinstance(t, transforms.RandomHorizontalFlip):
                  if random.random() < random_flip:
                      image = transforms.RandomHorizontalFlip(1)(image)
                      for idx, box in enumerate(targets["boxes"]):
                          # Flip box's x-coordinates
                          box[0] = width - box[0]
                          box[2] = width - box[2]
                          box[[0, 2]] = box[[2, 0]]
                          targets["boxes"][idx] = box
              else:
                  image = t(image)
  
          # Scale down box if necessary
          if scale_factor != 1.0:
              for idx, box in enumerate(targets["boxes"]):
                  box = (box / scale_factor).long()
                  targets["boxes"][idx] = box
  
      return image, targets
  
imports: import os
  import pandas
  import random
  import torch
  import torchvision
  from detecto.config import config
  from detecto.utils import default_transforms, filter_top_predictions, xml_to_csv, _is_iterable, read_image
  from torchvision import transforms
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the Dataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: image,  targets