class_docstr: Pre-trained ResNetV1b Model, which produces the strides of 8
  featuremaps at conv5.
  
  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."
code: def __init__(
      self,
      block,
      layers,
      classes=1000,
      dilated=False,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      last_gamma=False,
      deep_stem=False,
      stem_width=32,
      avg_down=False,
      final_drop=0.0,
      use_global_stats=False,
      name_prefix="",
      **kwargs,
  ):
      self.inplanes = stem_width * 2 if deep_stem else 64
      super(ResNetV1b, self).__init__(prefix=name_prefix)
      norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
      if use_global_stats:
          norm_kwargs["use_global_stats"] = True
      self.norm_kwargs = norm_kwargs
      with self.name_scope():
          if not deep_stem:
              self.conv1 = nn.Conv2D(
                  channels=64,
                  kernel_size=7,
                  strides=2,
                  padding=3,
                  use_bias=False,
              )
          else:
              self.conv1 = nn.HybridSequential(prefix="conv1")
              self.conv1.add(
                  nn.Conv2D(
                      channels=stem_width,
                      kernel_size=3,
                      strides=2,
                      padding=1,
                      use_bias=False,
                  )
              )
              self.conv1.add(
                  norm_layer(in_channels=stem_width, **norm_kwargs)
              )
              self.conv1.add(nn.Activation("relu"))
              self.conv1.add(
                  nn.Conv2D(
                      channels=stem_width,
                      kernel_size=3,
                      strides=1,
                      padding=1,
                      use_bias=False,
                  )
              )
              self.conv1.add(
                  norm_layer(in_channels=stem_width, **norm_kwargs)
              )
              self.conv1.add(nn.Activation("relu"))
              self.conv1.add(
                  nn.Conv2D(
                      channels=stem_width * 2,
                      kernel_size=3,
                      strides=1,
                      padding=1,
                      use_bias=False,
                  )
              )
          self.bn1 = norm_layer(
              in_channels=64 if not deep_stem else stem_width * 2,
              **norm_kwargs,
          )
          self.relu = nn.Activation("relu")
          self.maxpool = nn.MaxPool2D(pool_size=3, strides=2, padding=1)
          self.layer1 = self._make_layer(
              1,
              block,
              64,
              layers[0],
              avg_down=avg_down,
              norm_layer=norm_layer,
              last_gamma=last_gamma,
          )
          self.layer2 = self._make_layer(
              2,
              block,
              128,
              layers[1],
              strides=2,
              avg_down=avg_down,
              norm_layer=norm_layer,
              last_gamma=last_gamma,
          )
          if dilated:
              self.layer3 = self._make_layer(
                  3,
                  block,
                  256,
                  layers[2],
                  strides=1,
                  dilation=2,
                  avg_down=avg_down,
                  norm_layer=norm_layer,
                  last_gamma=last_gamma,
              )
              self.layer4 = self._make_layer(
                  4,
                  block,
                  512,
                  layers[3],
                  strides=1,
                  dilation=4,
                  avg_down=avg_down,
                  norm_layer=norm_layer,
                  last_gamma=last_gamma,
              )
          else:
              self.layer3 = self._make_layer(
                  3,
                  block,
                  256,
                  layers[2],
                  strides=2,
                  avg_down=avg_down,
                  norm_layer=norm_layer,
                  last_gamma=last_gamma,
              )
              self.layer4 = self._make_layer(
                  4,
                  block,
                  512,
                  layers[3],
                  strides=2,
                  avg_down=avg_down,
                  norm_layer=norm_layer,
                  last_gamma=last_gamma,
              )
          self.avgpool = nn.GlobalAvgPool2D()
          self.flat = nn.Flatten()
          self.drop = None
          if final_drop > 0.0:
              self.drop = nn.Dropout(final_drop)
          self.fc = nn.Dense(
              in_units=512 * block.expansion, units=classes
          )
  
imports: from __future__ import division
  from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 1 to create a new ResNetV1b object from inherited base classes: HybridBlock with example: obj = ResNetV1b(block, layers, classes, dilated, norm_layer, norm_kwargs, last_gamma, deep_stem, stem_width, avg_down, final_drop, use_global_stats, name_prefix)