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 __init__(
      self,
      backbone,
      pretrained_base,
      num_input_images=1,
      scales=range(4),
      num_output_channels=1,
      use_skips=True,
      ctx=cpu(),
      **kwargs,
  ):
      super(MonoDepth2, self).__init__()
  
      with self.name_scope():
          self.encoder = ResnetEncoder(
              backbone,
              pretrained_base,
              num_input_images=num_input_images,
              ctx=ctx,
          )
          if not pretrained_base:
              self.encoder.initialize(ctx=ctx)
          self.decoder = DepthDecoder(
              self.encoder.num_ch_enc,
              scales,
              num_output_channels,
              use_skips,
          )
          self.decoder.initialize(ctx=ctx)
  
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 1 to create a new MonoDepth2 object from inherited base classes: nn with example: obj = MonoDepth2(backbone, pretrained_base, num_input_images, scales, num_output_channels, use_skips, ctx)