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: 1)
  scales: list
      The scales used in the loss. (Default: range(4))
  num_output_channels: int
      The number of output channels. (Default: 1)
  use_skips: bool
      This will use skip architecture in the network. (Default: True)
  
  Reference:
  
      Clement Godard, Oisin Mac Aodha, Michael Firman, Gabriel Brostow.
      "Digging Into Self-Supervised Monocular Depth Estimation." ICCV, 2019
  
  Examples
  --------
  >>> model = MonoDepth2(backbone='resnet18', pretrained_base=True)
  >>> print(model)
code: def demo(self, x):
      return self.predict(x)
  
file_path: gluon-cv/gluoncv/model_zoo/monodepthv2/monodepth2.py
imports: from mxnet.gluon import nn
  from mxnet.context import cpu
  from .resnet_encoder import ResnetEncoder
  from .depth_decoder import DepthDecoder
label_desc: Use image node_id 3 for calling the MonoDepth2 obj's underlying member method code with example usage: obj.demo(x) and returns: self