code: def _generate_annotation(example_id, height, width):
      """Generate a fake annotation XML for the given example id."""
      # pylint: disable=protected-access
      label_names = tfds.features.ClassLabel(
          names=bccd_dataset_builder._CLASS_LABELS
      ).names  # pytype: disable=module-attr
      # pylint: enable=protected-access
      annotation = "<annotation>\n"
      annotation += "<folder>JPEGImages</folder>\n"
      annotation += "<filename>%d.jpg</filename>\n" % example_id
      annotation += (
          "<path>/home/pi/detection_dataset/JPEGImages/%d.jpg</path>"
          % example_id
      )
      annotation += "<source>\n"
      annotation += "<database>Unknown</database>\n"
      annotation += "</source>"
      annotation += "<size>\n"
      annotation += "<width>%d</width>\n" % width
      annotation += "<height>%d</height>\n" % height
      annotation += "</size>\n"
      for i in range(random.randint(MIN_NUM_OBJECTS, MAX_NUM_OBJECTS)):
          annotation += "<object>\n"
          annotation += "  <name>%s</name>\n" % random.choice(
              label_names
          )
          annotation += "  <pose>Unspecified</pose>\n"
          annotation += "  <truncated>0</truncated>\n"
          if i > 0:
              annotation += (
                  "  <difficult>%s</difficult>\n" % random.randint(0, 1)
              )
          else:
              annotation += "  <difficult>0</difficult>\n"
          obj_w = random.randint(
              MIN_OBJECT_HEIGHT_WIDTH, MAX_OBJECT_HEIGHT_WIDTH
          )
          obj_h = random.randint(
              MIN_OBJECT_HEIGHT_WIDTH, MAX_OBJECT_HEIGHT_WIDTH
          )
          obj_x = random.randint(0, width - obj_w)
          obj_y = random.randint(0, height - obj_h)
          annotation += "  <bndbox>\n"
          annotation += "    <xmin>%d</xmin>\n" % obj_x
          annotation += "    <ymin>%d</ymin>\n" % obj_y
          annotation += "    <xmax>%d</xmax>\n" % (obj_x + obj_w - 1)
          annotation += "    <ymax>%d</ymax>\n" % (obj_y + obj_h - 1)
          annotation += "  </bndbox>\n"
          annotation += "</object>\n"
      annotation += "</annotation>\n"
      # Add annotation XML to the tar file.
      filepath = os.path.join(
          _output_dir(),
          "BCCD_Dataset-1.0/BCCD/Annotations/BloodImage_{:05d}.xml".format(
              example_id
          ),
      )
      _write_text_file(filepath, annotation)
  
file_path: datasets/tensorflow_datasets/datasets/bccd/dummy_data_generation.py
imports: import os
  import random
  from absl import app
  from absl import flags
  from tensorflow_datasets.core import utils
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  from tensorflow_datasets.datasets.bccd import bccd_dataset_builder
  import tensorflow_datasets.public_api
  from tensorflow_datasets.testing import fake_data_utils
label_desc: Use image node_id 4 for calling a global function with example usage: _generate_annotation(example_id, height, width) without return types