class_docstr: High Resolution Branch of MODNet
class_docstr_tok: ["High","Resolution","Branch","of","MODNet"]
code: def __init__(self, hr_channels, enc_channels):
      super().__init__()
  
      self.tohr_enc2x = Conv2dIBNormRelu(
          enc_channels[0], hr_channels, 1, stride=1, padding=0
      )
      self.conv_enc2x = Conv2dIBNormRelu(
          hr_channels + 3, hr_channels, 3, stride=2, padding=1
      )
  
      self.tohr_enc4x = Conv2dIBNormRelu(
          enc_channels[1], hr_channels, 1, stride=1, padding=0
      )
      self.conv_enc4x = Conv2dIBNormRelu(
          2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1
      )
  
      self.conv_hr4x = nn.Sequential(
          Conv2dIBNormRelu(
              2 * hr_channels + enc_channels[2] + 3,
              2 * hr_channels,
              3,
              stride=1,
              padding=1,
          ),
          Conv2dIBNormRelu(
              2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1
          ),
          Conv2dIBNormRelu(
              2 * hr_channels, hr_channels, 3, stride=1, padding=1
          ),
      )
  
      self.conv_hr2x = nn.Sequential(
          Conv2dIBNormRelu(
              2 * hr_channels, 2 * hr_channels, 3, stride=1, padding=1
          ),
          Conv2dIBNormRelu(
              2 * hr_channels, hr_channels, 3, stride=1, padding=1
          ),
          Conv2dIBNormRelu(
              hr_channels, hr_channels, 3, stride=1, padding=1
          ),
          Conv2dIBNormRelu(
              hr_channels, hr_channels, 3, stride=1, padding=1
          ),
      )
  
      self.conv_hr = nn.Sequential(
          Conv2dIBNormRelu(
              hr_channels + 3, hr_channels, 3, stride=1, padding=1
          ),
          Conv2dIBNormRelu(
              hr_channels,
              1,
              1,
              stride=1,
              padding=0,
              with_ibn=False,
              with_relu=False,
          ),
      )
  
imports: from collections import defaultdict
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  import scipy
  import paddleseg
  from paddleseg.models import layers, losses
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 1 to create a new HRBranch object from inherited base classes: nn with example: obj = HRBranch(hr_channels, enc_channels)