code: def faster_rcnn_fpn_syncbn_resnest269_coco(
      pretrained=False, pretrained_base=True, num_devices=0, **kwargs
  ):
      r"""Faster R-CNN with ResNeSt
      ResNeSt: Split Attention Network"
  
      Parameters
      ----------
      pretrained : bool or str
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      pretrained_base : bool or str, optional, default is True
          Load pretrained base network, the extra layers are randomized. Note that
          if pretrained is `Ture`, this has no effect.
      num_devices : int, default is 0
          Number of devices for sync batch norm layer. if less than 1, use all devices available.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
  
      Examples
      --------
      >>> model = get_faster_rcnn_fpn_syncbn_resnest269_coco(pretrained=True)
      >>> print(model)
      """
      from ....model_zoo.resnest import resnest269
      from ....data import COCODetection
  
      classes = COCODetection.CLASSES
      pretrained_base = False if pretrained else pretrained_base
      gluon_norm_kwargs = (
          {"num_devices": num_devices} if num_devices >= 1 else {}
      )
      base_network = resnest269(
          pretrained=pretrained_base,
          dilated=False,
          use_global_stats=False,
          norm_layer=SyncBatchNorm,
          norm_kwargs=gluon_norm_kwargs,
      )
      from gluoncv.nn.dropblock import set_drop_prob
      from functools import partial
  
      apply_drop_prob = partial(set_drop_prob, 0.0)
      base_network.apply(apply_drop_prob)
      sym_norm_kwargs = (
          {"ndev": num_devices} if num_devices >= 1 else {}
      )
      features = FPNFeatureExpander(
          network=base_network,
          outputs=[
              "layers1_relu11_fwd",
              "layers2_relu119_fwd",
              "layers3_relu191_fwd",
              "layers4_relu31_fwd",
          ],
          num_filters=[256, 256, 256, 256],
          use_1x1=True,
          use_upsample=True,
          use_elewadd=True,
          use_p6=True,
          no_bias=True,
          pretrained=pretrained_base,
          norm_layer=mx.sym.contrib.SyncBatchNorm,
          norm_kwargs=sym_norm_kwargs,
      )
      top_features = None
      # 1 Conv 1 FC layer before RCNN cls and reg
      box_features = nn.HybridSequential()
      for _ in range(4):
          box_features.add(
              nn.Conv2D(256, 3, padding=1, use_bias=False),
              SyncBatchNorm(**gluon_norm_kwargs),
              nn.Activation("relu"),
          )
      box_features.add(
          nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)),
          nn.Activation("relu"),
      )
  
      train_patterns = (
          "(?!.*moving)"  # excluding symbol bn moving mean and var
      )
      return get_faster_rcnn(
          name="fpn_syncbn_resnest269",
          dataset="coco",
          pretrained=pretrained,
          features=features,
          top_features=top_features,
          classes=classes,
          box_features=box_features,
          short=(640, 864),
          max_size=1440,
          min_stage=2,
          max_stage=6,
          train_patterns=train_patterns,
          nms_thresh=0.5,
          nms_topk=-1,
          post_nms=-1,
          roi_mode="align",
          roi_size=(7, 7),
          strides=(4, 8, 16, 32, 64),
          clip=4.14,
          rpn_channel=256,
          base_size=16,
          scales=(2, 4, 8, 16, 32),
          ratios=(0.5, 1, 2),
          alloc_size=(384, 384),
          rpn_nms_thresh=0.7,
          rpn_train_pre_nms=12000,
          rpn_train_post_nms=2000,
          rpn_test_pre_nms=6000,
          rpn_test_post_nms=1000,
          rpn_min_size=1,
          num_sample=512,
          pos_iou_thresh=0.5,
          pos_ratio=0.25,
          max_num_gt=100,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  import warnings
  import mxnet
  from mxnet.gluon import nn
  from mxnet.gluon.contrib.nn import SyncBatchNorm
  from ..faster_rcnn import get_faster_rcnn
  from .doublehead_rcnn import get_doublehead_rcnn
  from ....nn.feature import FPNFeatureExpander
label_desc: Use image node_id 14 for calling a global function with example usage: faster_rcnn_fpn_syncbn_resnest269_coco(pretrained, pretrained_base, num_devices) and returns: get_faster_rcnn