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 hybrid_forward(self, F, x):
      x = self.features(x)
      x = self.output(x)
  
      return x
  
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 3 for calling the ResNetV1 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x