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: Define the DALI graph.
docstr_tok: ["Define","the","DALI","graph","."]
code: def define_graph(self):
      """
      Define the DALI graph.
      """
      saturation = self.rng1()
      contrast = self.rng1()
      brightness = self.rng2()
      hue = self.rng3()
      coin_rnd = self.flip_coin()
  
      images, bboxes, labels = self.dataset_reader()
  
      crop_begin, crop_size, bboxes, labels = self.crop(bboxes, labels)
      images = self.slice(images, crop_begin, crop_size)
  
      images = self.flip(images, horizontal=coin_rnd)
      bboxes = self.bbflip(bboxes, horizontal=coin_rnd)
      images = self.resize(images)
      images = images.gpu()
      images = self.twist(
          images,
          saturation=saturation,
          contrast=contrast,
          brightness=brightness,
          hue=hue,
      )
      images = self.normalize(images)
      bboxes, labels = self.box_encoder(bboxes, labels)
  
      return (images, bboxes.gpu(), labels.gpu())
  
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 3 for calling the SSDDALIPipeline obj's underlying member method code with example usage: obj.define_graph() and returns: images,  bboxes,  labels