class_docstr: Batchify FasterRCNN data with 5 elements: img, bbox, rpn_cls_targets, rpn_box_targets,
  and rpn_box_masks
  Parameters
  ----------
  net : mxnet.gluon.HybridBlock.
      The faster-rcnn network used to infer output shape.
  num_shards : int, default 1
      Number of shards. Each shard corresponds to one device.
class_docstr_tok: ["Batchify","FasterRCNN","data","with","5","elements",":","img",",","bbox",",","rpn_cls_targets",",","rpn_box_targets",",","and","rpn_box_masks","Parameters","--","--","--","--","--","net",":","mxnet.gluon.HybridBlock",".","The","faster-rcnn","network","used","to","infer","output","shape",".","num_shards",":","int",",","default","1","Number","of","shards",".","Each","shard","corresponds","to","one","device","."]
code: def __init__(self, net, num_shards=1):
      self._feat_sym = net.features(mx.sym.var(name="data"))
      self._num_shards = num_shards
      self._img_pad = Pad(
          axis=(1, 2), num_shards=num_shards, ret_length=False
      )
      self._label_pad = Pad(
          pad_val=-1, num_shards=num_shards, ret_length=False
      )
      self.NUM_ELEMENTS = 5
  
imports: import logging
  import numpy
  import mxnet
label_desc: Use image node_id 1 to create a new FasterRCNNTrainBatchify object from inherited base classes: object with example: obj = FasterRCNNTrainBatchify(net, num_shards)