class_docstr: DALI Pipeline with SSD training transform.
  
  Parameters
  ----------
  device_id: int
       DALI pipeline arg - Device id.
  num_workers:
      DALI pipeline arg - Number of CPU workers.
  batch_size:
      Batch size.
  data_shape: int
      Height and width length. (height==width in SSD)
  anchors: float list
      Normalized [ltrb] anchors generated from SSD networks.
      The shape length be ``N*4`` since it is a list of the N anchors that have
      all 4 float elements.
  dataset_reader: float
      Partial pipeline object, which __call__ function has to return
      (images, bboxes, labels) DALI EdgeReference tuple.
  seed: int
      Random seed. Default value is -1, which corresponds to no seed.
docstr: Prepare anchors into ltrb (normalized DALI anchors format list)
docstr_tok: ["Prepare","anchors","into","ltrb","(","normalized","DALI","anchors","format","list",")"]
code: def _to_normalized_ltrb_list(self, anchors, size):
      """Prepare anchors into ltrb (normalized DALI anchors format list)"""
      if isinstance(anchors, list):
          return anchors
      anchors_np = anchors.squeeze().asnumpy()
      anchors_np_ltrb = anchors_np.copy()
      anchors_np_ltrb[:, 0] = anchors_np[:, 0] - 0.5 * anchors_np[:, 2]
      anchors_np_ltrb[:, 1] = anchors_np[:, 1] - 0.5 * anchors_np[:, 3]
      anchors_np_ltrb[:, 2] = anchors_np[:, 0] + 0.5 * anchors_np[:, 2]
      anchors_np_ltrb[:, 3] = anchors_np[:, 1] + 0.5 * anchors_np[:, 3]
      anchors_np_ltrb /= size
      return anchors_np_ltrb.flatten().tolist()
  
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import experimental
  from ....utils import try_import_dali
label_desc: Use image node_id 2 for calling the SSDDALIPipeline obj's underlying member method code with example usage: obj._to_normalized_ltrb_list(anchors, size) and returns: anchors_np_ltrb, anchors