code: def residualattentionnet164(**kwargs):
      r"""AttentionModel model from
      `"Residual Attention Network for Image Classification"
      <https://arxiv.org/pdf/1704.06904.pdf>`_ paper.
  
      Parameters
      ----------
      input_size : int
          Input size of net. Options are 32,224.
      num_layers : int
          Numbers of layers. Options are 56, 92, 128, 164, 200, 236, 452.
      pretrained : bool, default False
          Whether to load the pretrained weights for model.
      ctx : Context, default CPU
          The context in which to load the pretrained weights.
      root : str, default '~/.mxnet/models'
          Location for keeping the model parameters.
      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`.
      """
  
      return get_residualAttentionModel(224, 164, **kwargs)
  
imports: from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.block import HybridBlock
label_desc: Use image node_id 7 for calling a global function with example usage: residualattentionnet164() and returns: get_residualAttentionModel