class_docstr: ResNeSt Model
  Parameters
  ----------
  block : Block
      Class for the residual block. Options are BasicBlockV1, BottleneckV1.
  layers : list of int
      Numbers of layers in each block
  classes : int, default 1000
      Number of classification classes.
  dilated : bool, default False
      Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
      typically used in Semantic Segmentation.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  last_gamma : bool, default False
      Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
  deep_stem : bool, default False
      Whether to replace the 7x7 conv1 with 3 3x3 convolution layers.
  avg_down : bool, default False
      Whether to use average pooling for projection skip connection between stages/downsample.
  final_drop : float, default 0.0
      Dropout ratio before the final classification layer.
  use_global_stats : bool, default False
      Whether forcing BatchNorm to use global statistics instead of minibatch statistics;
      optionally set to True if finetuning using ImageNet classification pretrained models.
  Reference:
      - He, Kaiming, et al. "Deep residual learning for image recognition."
      Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
      - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
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code: def hybrid_forward(self, F, x):
      x = self.conv1(x)
      x = self.bn1(x)
      x = self.relu(x)
      x = self.maxpool(x)
  
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      x = self.layer4(x)
  
      x = self.avgpool(x)
      x = self.flat(x)
      if self.drop is not None:
          x = self.drop(x)
      x = self.fc(x)
  
      return x
  
imports: from __future__ import division
  import math
  from mxnet.context import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon.nn import BatchNorm
  from ..nn.dropblock import DropBlock
  from ..nn.splat import SplitAttentionConv
label_desc: Use image node_id 3 for calling the ResNeSt obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x