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 CIFARBasicBlockV1, 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 10
      Number of classification classes.
  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`.
class_docstr_tok: ["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","CIFARBasicBlockV1",",","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","10","Number","of","classification","classes",".","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=10,
      norm_layer=BatchNorm,
      norm_kwargs=None,
      **kwargs,
  ):
      super(CIFARResNetV1, self).__init__(**kwargs)
      assert len(layers) == len(channels) - 1
      with self.name_scope():
          self.features = nn.HybridSequential(prefix="")
          self.features.add(
              nn.Conv2D(channels[0], 3, 1, 1, use_bias=False)
          )
          self.features.add(
              norm_layer(**({} if norm_kwargs is None else norm_kwargs))
          )
  
          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],
                      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
  import os
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet import cpu
label_desc: Use image node_id 1 to create a new CIFARResNetV1 object from inherited base classes: HybridBlock with example: obj = CIFARResNetV1(block, layers, channels, classes, norm_layer, norm_kwargs)