class_docstr: AttentionModel 56 model from
  `"Residual Attention Network for Image Classification"
  <https://arxiv.org/pdf/1704.06904.pdf>`_ paper.
  Input size is 56 x 56.
  Default size is for 56 stage input.
  If input size is different you need to change it suiting for your input size.
  
  Parameters
  ----------
  channels : int
      Output channels.
  size1 : int, default 56
      Upsampling size1.
  size2 : int, default 28
      Upsampling size2.
  size3 : int, default 14
      Upsampling size3.
  scale : tuple, default (1, 2, 1)
      Network scale p, t, r.
  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: ["AttentionModel","56","model","from","`","``","Residual","Attention","Network","for","Image","Classification","''","<","https",":","\/\/arxiv.org\/pdf\/1704.06904.pdf",">","`","_","paper",".","Input","size","is","56","x","56",".","Default","size","is","for","56","stage","input",".","If","input","size","is","different","you","need","to","change","it","suiting","for","your","input","size",".","Parameters","--","--","--","--","--","channels",":","int","Output","channels",".","size1",":","int",",","default","56","Upsampling","size1",".","size2",":","int",",","default","28","Upsampling","size2",".","size3",":","int",",","default","14","Upsampling","size3",".","scale",":","tuple",",","default","(","1",",","2",",","1",")","Network","scale","p",",","t",",","r.","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.first_residual_blocks(x)
      out_trunk = self.trunk_branches(x)
  
      out_mpool1 = self.mpool1(x)
      out_softmax1 = self.softmax1_blocks(out_mpool1)
      out_skip1_connection = self.skip1_connection_residual_block(
          out_softmax1
      )
  
      out_mpool2 = self.mpool2(out_softmax1)
      out_softmax2 = self.softmax2_blocks(out_mpool2)
      out_skip2_connection = self.skip2_connection_residual_block(
          out_softmax2
      )
  
      out_mpool3 = self.mpool3(out_softmax2)
      out_softmax3 = self.softmax3_blocks(out_mpool3)
  
      out_interp3 = F.elemwise_add(
          self.interpolation3(out_softmax3), out_softmax2
      )
      out = F.elemwise_add(out_interp3, out_skip2_connection)
  
      out_softmax4 = self.softmax4_blocks(out)
      out_interp2 = F.elemwise_add(
          self.interpolation2(out_softmax4), out_softmax1
      )
      out = F.elemwise_add(out_interp2, out_skip1_connection)
  
      out_softmax5 = self.softmax5_blocks(out)
      out_interp1 = F.elemwise_add(
          self.interpolation1(out_softmax5), out_trunk
      )
  
      out_softmax6 = self.softmax6_blocks(out_interp1)
      out = F.elemwise_add(F.ones_like(out_softmax6), out_softmax6)
      out = F.elemwise_mul(out, out_trunk)
  
      out_last = self.last_blocks(out)
      return out_last
  
imports: from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 2 for calling the AttentionModule_stage1 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out_last