code: def __getitem__(self, item):
      image = Image.open(self.image_paths[item])
      bboxes = self.bounding_boxes[item]
  
      image = np.array(image)
      if self.augmentations is not None:
          augmented = self.augmentations(image=image, bboxes=bboxes)
          image = augmented["image"]
          bboxes = augmented["bboxes"]
  
      bboxes = [b[:4] for b in bboxes]
      image = np.transpose(image, (2, 0, 1)).astype(np.float32)
      bboxes = np.array(bboxes)
  
      bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]
      bboxes[:, 3] = bboxes[:, 1] + bboxes[:, 3]
  
      area = (bboxes[:, 3] - bboxes[:, 1]) * (
          bboxes[:, 2] - bboxes[:, 0]
      )
      labels = torch.ones((bboxes.shape[0],), dtype=torch.int64)
      is_crowd = torch.zeros((bboxes.shape[0],), dtype=torch.int64)
  
      _, new_h, new_w = image.shape
  
      target = {
          "bboxes": torch.as_tensor(
              bboxes.tolist(), dtype=torch.float32
          ),
          "area": torch.as_tensor(area.tolist(), dtype=torch.float32),
          "iscrowd": is_crowd,
          "labels": labels,
          "img_size": torch.tensor([new_h, new_w], dtype=torch.int64),
          "img_scale": torch.tensor([1.0]),
      }
  
      image = torch.tensor(image, dtype=torch.float)
  
      return image, target
  
imports: import argparse
  import ast
  import os
  import albumentations
  import numpy
  import pandas
  import timm
  import torch
  import torch.nn
  import torchvision
  from effdet import DetBenchTrain, EfficientDet, get_efficientdet_config
  from effdet.config.model_config import efficientdet_model_param_dict
  from effdet.efficientdet import HeadNet
  from PIL import Image, ImageFile
  from sklearn.model_selection import train_test_split
  from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
  from tez import Tez, TezConfig
label_desc: Use image node_id 3 for calling the WheatDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: image,  target