class_docstr: AttentionModel model from
  `"Residual Attention Network for Image Classification"
  <https://arxiv.org/pdf/1704.06904.pdf>`_ paper.
  Input size is 224 x 224.
  
  Parameters
  ----------
  scale : tuple
      Network scale p, t, r.
  m : tuple
      Network scale m.Network scale is defined as 36m + 20.
      And normally m is a tuple of (m-1, m, m+1) except m==1 as (1, 1, 1).
  classes : int, default 1000
      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`.
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code: def hybrid_forward(self, F, x):
      x = self.conv1(x)
      x = self.mpool1(x)
      x = self.residual_block1(x)
      x = self.attention_module1(x)
      x = self.residual_block2(x)
      x = self.attention_module2(x)
      x = self.residual_block3(x)
      x = self.attention_module3(x)
      x = self.residual_block4(x)
      x = self.residual_block5(x)
      x = self.residual_block6(x)
      x = self.mpool2(x)
      x = self.fc(x)
      x = F.Flatten(x)
      return x
  
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 ResidualAttentionModel obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x