code: def get_monodepth2posenet(
      backbone="resnet18",
      pretrained_base=True,
      num_input_images=2,
      num_input_features=1,
      num_frames_to_predict_for=2,
      stride=1,
      root="~/.mxnet/models",
      ctx=cpu(0),
      pretrained=False,
      pretrained_model="kitti_stereo_640x192",
      **kwargs,
  ):
      r"""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)
  
      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 = MonoDepth2PoseNet(
          backbone=backbone,
          pretrained_base=pretrained_base,
          num_input_images=num_input_images,
          num_input_features=num_input_features,
          num_frames_to_predict_for=num_frames_to_predict_for,
          stride=stride,
          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_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 1 for calling a global function with example usage: get_monodepth2posenet(backbone, pretrained_base, num_input_images, num_input_features, num_frames_to_predict_for, stride, root, ctx, pretrained, pretrained_model) and returns: model