class_docstr: SE_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 SE_BasicBlockV1, SE_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.
  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: ["SE_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","SE_BasicBlockV1",",","SE_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",".","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
  import os
  from mxnet import cpu
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 3 for calling the SE_ResNetV1 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x