class_docstr: Encoder of Monodepth2
  
  Parameters
  ----------
  backbone : string
      Pre-trained dilated backbone network type ('resnet18', 'resnet34', 'resnet50',
      'resnet101' or 'resnet152').
  pretrained : 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)
  root : str, default '~/.mxnet/models'
      Location for keeping the model parameters.
class_docstr_tok: ["Encoder","of","Monodepth2","Parameters","--","--","--","--","--","backbone",":","string","Pre-trained","dilated","backbone","network","type","(","'resnet18","'",",","'resnet34","'",",","'resnet50","'",",","'resnet101","'","or","'resnet152","'",")",".","pretrained",":","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",")","root",":","str",",","default","'~\/.mxnet\/models'","Location","for","keeping","the","model","parameters","."]
code: def predict(self, input_image):
      # pylint: disable=unused-argument, missing-function-docstring
      self.features = []
      x = (input_image - 0.45) / 0.225
      x = self.encoder.conv1(x)
      x = self.encoder.bn1(x)
      self.features.append(self.encoder.relu(x))
      self.features.append(
          self.encoder.layer1(self.encoder.maxpool(self.features[-1]))
      )
      self.features.append(self.encoder.layer2(self.features[-1]))
      self.features.append(self.encoder.layer3(self.features[-1]))
      self.features.append(self.encoder.layer4(self.features[-1]))
  
      return self.features
  
file_path: gluon-cv/gluoncv/model_zoo/monodepthv2/resnet_encoder.py
imports: from __future__ import absolute_import, division, print_function
  import os
  import numpy
  import mxnet
  from mxnet.gluon import nn
  from mxnet.context import cpu
  from ...model_zoo.resnetv1b import resnet18_v1b, resnet34_v1b, resnet50_v1s, resnet101_v1s, resnet152_v1s
label_desc: Use image node_id 3 for calling the ResnetEncoder obj's underlying member method code with example usage: obj.predict(input_image) and returns: self