class_docstr: Monodepth2
  
  Parameters
  ----------
  backbone : string
      Pre-trained dilated backbone network type ('resnet18', 'resnet34', 'resnet50',
      'resnet101' or 'resnet152').
  pretrained_base : bool or str
      Refers to if the backbone is pretrained or not. If `True`,
      model weights of a model that was trained on ImageNet is loaded.
  num_input_images : int
      The number of input sequences. 1 for depth encoder, larger than 1 for pose encoder.
      (Default: 2)
  num_input_features : int
      The number of input feature maps from posenet encoder. (Default: 1)
  num_frames_to_predict_for: int
      The number of output pose between frames; If None, it equals num_input_features - 1.
      (Default: 2)
  stride: int
      The stride number for Conv in pose decoder. (Default: 1)
  
  Reference:
  
      Clement Godard, Oisin Mac Aodha, Michael Firman, Gabriel Brostow.
      "Digging Into Self-Supervised Monocular Depth Estimation." ICCV, 2019
  
  Examples
  --------
  >>> model = MonoDepth2PoseNet(backbone='resnet18', pretrained_base=True)
  >>> print(model)
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code: def demo(self, x):
      return self.predict(x)
  
file_path: gluon-cv/gluoncv/model_zoo/monodepthv2/monodepth2_posenet.py
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from .resnet_encoder import ResnetEncoder
  from .pose_decoder import PoseDecoder
label_desc: Use image node_id 3 for calling the MonoDepth2PoseNet obj's underlying member method code with example usage: obj.demo(x) and returns: self