class_docstr: DeepLabV3+.
  
  Args:
      dataset(str): Specify a training dataset name from 'voc' or 'voc-coco'.
      output_stride(int): DeepLabV3 uses atrous (a.k.a. dilated) convolutions. The atrous rate depends on the output stride. the output stride has 
                   to be selected from 8 or 16. Default is 8. If the output_stride is 8 the atrous rate will be [12,24,36] 
                   and if the output_stride is 16 the atrous rate will be [6,12,18].
  
  The following is a list of string that can be specified to ``use_up_to`` option in ``__call__`` method;
  
  * ``'segmentation'`` (default): The output of the final layer.
  * ``'lastconv'``: The output from last Convolution.
  * ``'lastconv+relu'``: Network up to ``'lastconv'`` followed by ReLU activation.
  
  References:
      * `Chen et al., Rethinking Atrous Convolution for Semantic Image Segmentation.
        <https://arxiv.org/abs/1706.05587>`_
code: def _input_shape(self):
      return (3, 513, 513)
  
file_path: nnabla/python/src/nnabla/models/semantic_segmentation/deeplabv3plus.py
imports: from __future__ import absolute_import
  from nnabla.utils.nnp_graph import NnpNetworkPass
  from .base import SemanticSegmentation
label_desc: Use image node_id 2 for calling the DeepLabV3plus obj's underlying member method code with example usage: obj._input_shape() and returns: int,  int,  int