class_docstr: AttentionModel 56 model from
  `"Residual Attention Network for Image Classification"
  <https://arxiv.org/pdf/1704.06904.pdf>`_ paper.
  Input size is 14 x 14.
  
  Parameters
  ----------
  channels : int
      Output channels.
  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`.
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code: def hybrid_forward(self, F, x):
      x = self.first_residual_blocks(x)
      out_trunk = self.trunk_branches(x)
      out_softmax1 = self.softmax1_blocks(x)
      out_softmax2 = self.softmax2_blocks(out_softmax1)
      out = F.elemwise_add(F.ones_like(out_softmax2), out_softmax2)
      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_stage4 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: out_last