code: def _nms_helper(draw):
      img_width = draw(st.integers(250, 1250))
      img_height = draw(st.integers(250, 1250))
      num_boxes = draw(st.integers(5, 50))
      bbox = {}
      for _ in range(num_boxes):
          x1 = draw(st.integers(0, img_width - 20))
          w = draw(st.integers(5, img_width - x1))
          y1 = draw(st.integers(0, img_height - 20))
          h = draw(st.integers(5, img_height - y1))
  
          bbox[(x1, y1, x1 + w, y1 + h)] = draw(st.floats(0.2, 1))
  
      iou_threshold = draw(st.floats(0.2, 1))
      max_output_size = draw(st.integers(1, num_boxes))
      score_threshold = draw(st.floats(0, 1))
      return (
          np.array(list(bbox.keys()), dtype=np.float32),
          np.array(list(bbox.values()), dtype=np.float32),
          iou_threshold,
          max_output_size,
          score_threshold,
      )
  
imports: from hypothesis import strategies, assume
  import ivy
  import numpy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test
  from ivy.functional.ivy.layers import _deconv_length, _pack_padded_sequence
label_desc: Use image node_id 6 for calling a global function with example usage: _nms_helper(draw) and returns: np,  np,  iou_threshold,  max_output_size,  score_threshold