class_docstr: ResNet V1 model from
  `"Deep Residual Learning for Image Recognition"
  <http://arxiv.org/abs/1512.03385>`_ paper.
  
  Parameters
  ----------
  block : gluon.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.
class_docstr_tok: ["ResNet","V1","model","from","`","``","Deep","Residual","Learning","for","Image","Recognition","''","<","http",":","\/\/arxiv.org\/abs\/1512.03385",">","`","_","paper",".","Parameters","--","--","--","--","--","block",":","gluon.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","."]
code: def _make_layer(self, block, layers, channels, stride, in_channels=0):
      layer = nn.HybridSequential()
      layer.add(
          block(
              channels,
              stride,
              channels != in_channels,
              in_channels=in_channels,
          )
      )
      for _ in range(layers - 1):
          layer.add(block(channels, 1, False, in_channels=channels))
      return layer
  
imports: import os
  from ....device import cpu
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
  from ....None import npx
label_desc: Use image node_id 2 for calling the ResNetV1 obj's underlying member method code with example usage: obj._make_layer(block, layers, channels, stride, in_channels) and returns: layer