code: def get_monodepth2(
      backbone="resnet18",
      pretrained_base=True,
      scales=range(4),
      num_output_channels=1,
      use_skips=True,
      root="~/.mxnet/models",
      ctx=cpu(0),
      pretrained=False,
      pretrained_model="kitti_stereo_640x192",
      **kwargs,
  ):
      r"""MonoDepth2
  
      Parameters
      ----------
      backbone : string, default:'resnet18'
          Pre-trained dilated backbone network type
          ('resnet18', 'resnet34', 'resnet50', 'resnet101' or 'resnet152').
      pretrained_base : bool or str, default: True
          This will load pretrained backbone network, that was trained on ImageNet.
  
      scales: list, default: range(4)
          The scales used in the loss.
      num_output_channels: int, default: 1
          The number of output channels.
      use_skips: bool, default: True
          This will use skip architecture in the network.
  
      ctx : Context, default: CPU
          The context in which to load the pretrained weights.
      root : str, default: '~/.mxnet/models'
          Location for keeping the model parameters.
      pretrained : bool or str, default: False
          Boolean value controls whether to load the default pretrained weights for model.
          String value represents the hashtag for a certain version of pretrained weights.
      pretrained_model : string, default: kitti_stereo_640x192
          The dataset that model pretrained on.
  
      """
  
      model = MonoDepth2(
          backbone=backbone,
          pretrained_base=pretrained_base,
          scales=scales,
          num_output_channels=num_output_channels,
          use_skips=use_skips,
          ctx=ctx,
          **kwargs,
      )
  
      if pretrained:
          from ...model_zoo.model_store import get_model_file
  
          model.load_parameters(
              get_model_file(
                  "monodepth2_%s_%s" % (backbone, pretrained_model),
                  tag=pretrained,
                  root=root,
              ),
              ctx=ctx,
          )
  
      return model
  
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 for calling a global function with example usage: get_monodepth2(backbone, pretrained_base, scales, num_output_channels, use_skips, root, ctx, pretrained, pretrained_model) and returns: model