class_docstr: Default SSD training transform which includes tons of image augmentations.
  
  Parameters
  ----------
  width : int
      Image width.
  height : int
      Image height.
  anchors : mxnet.nd.NDArray, optional
      Anchors generated from SSD networks, the shape must be ``(1, N, 4)``.
      Since anchors are shared in the entire batch so it is ``1`` for the first dimension.
      ``N`` is the number of anchors for each image.
  
      .. hint::
  
          If anchors is ``None``, the transformation will not generate training targets.
          Otherwise it will generate training targets to accelerate the training phase
          since we push some workload to CPU workers instead of GPUs.
  
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
  iou_thresh : float
      IOU overlap threshold for maximum matching, default is 0.5.
  box_norm : array-like of size 4, default is (0.1, 0.1, 0.2, 0.2)
      Std value to be divided from encoded values.
code: def _target_generator(self):
      # since we do not have predictions yet, so we ignore sampling here
      if self._internal_target_generator is None:
          if self._anchors_none:
              return None
          from ....model_zoo.ssd.target import SSDTargetGenerator
  
          self._internal_target_generator = SSDTargetGenerator(
              iou_thresh=self._iou_thresh,
              stds=self._box_norm,
              negative_mining_ratio=-1,
              **self._kwargs,
          )
          return self._internal_target_generator
      else:
          return self._internal_target_generator
  
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 SSDDefaultTrainTransform obj's underlying member method code with example usage: obj._target_generator() and returns: self, self, None