code: def _input(self, image_ids):
      image_data = self._coco.loadImgs(image_ids)
      images = np.zeros(
          (
              self._batch_size,
              self._image_size[0],
              self._image_size[1],
              3,
          ),
          dtype=np.float32,
      )
      bboxes = []
      classes = []
      for i in range(self._batch_size):
          image_path = os.path.join(
              self._file_root, image_data[i]["file_name"]
          )
          image_width = image_data[i]["width"]
          image_height = image_data[i]["height"]
          image = cv2.imread(image_path)
          image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
          image = tf.image.resize(image, self._image_size) / 255
          image = tf.reshape(
              image, (1, self._image_size[0], self._image_size[1], 3)
          )
          images[i, ...] = image
  
          ann_ids = self._coco.getAnnIds(image_ids[i])
          anns = self._coco.loadAnns(ann_ids)
          sample_bboxes = np.array([ann["bbox"] for ann in anns])
          if len(sample_bboxes.shape) == 1:
              sample_bboxes = np.zeros((0, 4))
          sample_bboxes[:, 0] /= image_width
          sample_bboxes[:, 1] /= image_height
          sample_bboxes[:, 2] /= image_width
          sample_bboxes[:, 3] /= image_height
          sample_bboxes[:, 0] += sample_bboxes[:, 2] / 2
          sample_bboxes[:, 1] += sample_bboxes[:, 3] / 2
  
          bboxes.append(sample_bboxes)
          classes.append(
              np.array([ann["category_id"] for ann in anns], dtype=int)
          )
  
      return images, bboxes, classes
  
file_path: DALI/docs/examples/use_cases/tensorflow/yolov4/src/np/pipeline.py
imports: from .coco import COCO
  import numpy
  import tensorflow
  import os
  import cv2
  import random
label_desc: Use image node_id 5 for calling the YOLOv4PipelineNumpy obj's underlying member method code with example usage: obj._input(image_ids) and returns: images,  bboxes,  classes