class_docstr: ResNet V1 model from
  `"Deep Residual Learning for Image Recognition"
  <http://arxiv.org/abs/1512.03385>`_ paper.
  
  Parameters
  ----------
  block : HybridBlock
      Class for the residual block. Options are BasicBlockV1, BottleneckV1.
  layers : list of int
      Numbers of layers in each block
  channels : list of int
      Numbers of channels in each block. Length should be one larger than layers list.
  classes : int, default 1000
      Number of classification classes.
  thumbnail : bool, default False
      Enable thumbnail.
  last_gamma : bool, default False
      Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
  use_se : bool, default False
      Whether to use Squeeze-and-Excitation module
  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`.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
code: def __init__(
      self,
      block,
      layers,
      channels,
      classes=1000,
      thumbnail=False,
      last_gamma=False,
      use_se=False,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(ResNetV1, self).__init__(**kwargs)
      assert len(layers) == len(channels) - 1
      with self.name_scope():
          self.features = nn.HybridSequential(prefix="")
          if thumbnail:
              self.features.add(_conv3x3(channels[0], 1, 0))
          else:
              self.features.add(
                  nn.Conv2D(channels[0], 7, 2, 3, use_bias=False)
              )
              self.features.add(
                  norm_layer(
                      **({} if norm_kwargs is None else norm_kwargs)
                  )
              )
              self.features.add(nn.Activation("relu"))
              self.features.add(nn.MaxPool2D(3, 2, 1))
  
          for i, num_layer in enumerate(layers):
              stride = 1 if i == 0 else 2
              self.features.add(
                  self._make_layer(
                      block,
                      num_layer,
                      channels[i + 1],
                      stride,
                      i + 1,
                      in_channels=channels[i],
                      last_gamma=last_gamma,
                      use_se=use_se,
                      norm_layer=norm_layer,
                      norm_kwargs=norm_kwargs,
                  )
              )
          self.features.add(nn.GlobalAvgPool2D())
  
          self.output = nn.Dense(classes, in_units=channels[-1])
  
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 ResNetV1 object from inherited base classes: HybridBlock with example: obj = ResNetV1(block, layers, channels, classes, thumbnail, last_gamma, use_se, norm_layer, norm_kwargs)