class_docstr: Taken from torchvision - slightly adjusted
class_docstr_tok: ["Taken","from","torchvision","-","slightly","adjusted"]
code: def __init__(
      self,
      growth_rate: int = 32,
      block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
      num_init_features: int = 64,
      bn_size: int = 4,
      drop_rate: float = 0,
      num_classes: int = 1000,
      memory_efficient: bool = False,
  ) -> None:
      super(DenseNet, self).__init__()
  
      # First convolution
      self.features = nn.Sequential(
          OrderedDict(
              [
                  (
                      "conv0",
                      nn.Conv2d(
                          3,
                          num_init_features,
                          kernel_size=3,
                          stride=2,
                          padding=1,
                          bias=False,
                      ),
                  ),
                  ("norm0", nn.BatchNorm2d(num_init_features)),
                  ("relu0", nn.ReLU(inplace=True)),
                  (
                      "pool0",
                      nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
                  ),
              ]
          )
      )
  
      # Each denseblock
      num_features = num_init_features
      for i, num_layers in enumerate(block_config):
          block = _DenseBlock(
              num_layers=num_layers,
              num_input_features=num_features,
              bn_size=bn_size,
              growth_rate=growth_rate,
              drop_rate=drop_rate,
              memory_efficient=memory_efficient,
          )
          self.features.add_module("denseblock%d" % (i + 1), block)
          num_features = num_features + num_layers * growth_rate
          if i != len(block_config) - 1:
              trans = _Transition(
                  num_input_features=num_features,
                  num_output_features=num_features // 2,
              )
              self.features.add_module("transition%d" % (i + 1), trans)
              num_features = num_features // 2
  
      # Final batch norm
      self.features.add_module("norm5", nn.BatchNorm2d(num_features))
  
      # Linear layer
      self.classifier = nn.Linear(num_features, num_classes)
  
      # Official init from torch repo.
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              nn.init.kaiming_normal_(m.weight)
          elif isinstance(m, nn.BatchNorm2d):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
          elif isinstance(m, nn.Linear):
              nn.init.constant_(m.bias, 0)
  
file_path: ARKitScenes/depth_upsampling/models/mspf/densenet.py
imports: from collections import OrderedDict
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from models.mspf.blocks.dense_net import _DenseBlock, _Transition
label_desc: Use image node_id 1 to create a new DenseNet object from inherited base classes: nn with example: obj = DenseNet(growth_rate, block_config, num_init_features, bn_size, drop_rate, num_classes, memory_efficient)