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 __init__(
      self,
      width,
      height,
      anchors=None,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
      iou_thresh=0.5,
      box_norm=(0.1, 0.1, 0.2, 0.2),
      **kwargs,
  ):
      self._width = width
      self._height = height
      self._anchors = anchors
      self._mean = mean
      self._std = std
      self._internal_target_generator = None
      self._iou_thresh = iou_thresh
      self._box_norm = box_norm
      self._kwargs = kwargs
      self._anchors_none = False
      if anchors is None:
          self._anchors_none = True
          return
  
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 1 to create a new SSDDefaultTrainTransform object from inherited base classes: object with example: obj = SSDDefaultTrainTransform(width, height, anchors, mean, std, iou_thresh, box_norm)