code: """
  Vanilla DenseNet implementation
  Paper: https://arxiv.org/abs/1608.06993
  Implementation taken from: https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py
  """
  import re
  from collections import OrderedDict
  from functools import partial
  from typing import Any, List, Optional, Tuple
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.checkpoint as cp
  from torch import Tensor
  
  class _DenseLayer(nn.Module):
      def __init__(
          self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False
      ) -> None:
          super().__init__()
          self.norm1: nn.BatchNorm2d
          self.add_module("norm1", nn.BatchNorm2d(num_input_features))
          self.relu1: nn.ReLU
          self.add_module("relu1", nn.ReLU(inplace=True))
          self.conv1: nn.Conv2d
          self.add_module(
              "conv1", nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)
          )
          self.norm2: nn.BatchNorm2d
          self.add_module("norm2", nn.BatchNorm2d(bn_size * growth_rate))
          self.relu2: nn.ReLU
          self.add_module("relu2", nn.ReLU(inplace=True))
          self.conv2: nn.Conv2d
          self.add_module(
              "conv2", nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)
          )
          self.drop_rate = float(drop_rate)
          self.memory_efficient = memory_efficient
  
      def bn_function(self, inputs: List[Tensor]) -> Tensor:
          concated_features = torch.cat(inputs, 1)
          bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
          return bottleneck_output
  
      # todo: rewrite when torchscript supports any
      def any_requires_grad(self, input: List[Tensor]) -> bool:
          for tensor in input:
              if tensor.requires_grad:
                  return True
          return False
  
      @torch.jit.unused  # noqa: T484
      def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor:
          def closure(*inputs):
              return self.bn_function(inputs)
  
          return cp.checkpoint(closure, *input)
  
      @torch.jit._overload_method  # noqa: F811
      def forward(self, input: List[Tensor]) -> Tensor:  # noqa: F811
          pass
  
      @torch.jit._overload_method  # noqa: F811
      def forward(self, input: Tensor) -> Tensor:  # noqa: F811
          pass
  
      # torchscript does not yet support *args, so we overload method
      # allowing it to take either a List[Tensor] or single Tensor
      def forward(self, input: Tensor) -> Tensor:  # noqa: F811
          if isinstance(input, Tensor):
              prev_features = [input]
          else:
              prev_features = input
  
          if self.memory_efficient and self.any_requires_grad(prev_features):
              if torch.jit.is_scripting():
                  raise Exception("Memory Efficient not supported in JIT")
  
              bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
          else:
              bottleneck_output = self.bn_function(prev_features)
  
          new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
          if self.drop_rate > 0:
              new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
          return new_features
  
  
  class _DenseBlock(nn.ModuleDict):
      _version = 2
  
      def __init__(
          self,
          num_layers: int,
          num_input_features: int,
          bn_size: int,
          growth_rate: int,
          drop_rate: float,
          memory_efficient: bool = False,
      ) -> None:
          super().__init__()
          for i in range(num_layers):
              layer = _DenseLayer(
                  num_input_features + i * growth_rate,
                  growth_rate=growth_rate,
                  bn_size=bn_size,
                  drop_rate=drop_rate,
                  memory_efficient=memory_efficient,
              )
              self.add_module("denselayer%d" % (i + 1), layer)
  
      def forward(self, init_features: Tensor) -> Tensor:
          features = [init_features]
          for name, layer in self.items():
              new_features = layer(features)
              features.append(new_features)
          return torch.cat(features, 1)
  
  
  class _Transition(nn.Sequential):
      def __init__(self, num_input_features: int, num_output_features: int) -> None:
          super().__init__()
          self.add_module("norm", nn.BatchNorm2d(num_input_features))
          self.add_module("relu", nn.ReLU(inplace=True))
          self.add_module("conv", nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
          self.add_module("pool", nn.AvgPool2d(kernel_size=2, stride=2))
  
  
  class DenseNet(nn.Module):
      r"""Densenet-BC model class, based on
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
      Args:
          growth_rate (int) - how many filters to add each layer (`k` in paper)
          block_config (list of 4 ints) - how many layers in each pooling block
          num_init_features (int) - the number of filters to learn in the first convolution layer
          bn_size (int) - multiplicative factor for number of bottle neck layers
            (i.e. bn_size * k features in the bottleneck layer)
          drop_rate (float) - dropout rate after each dense layer
          num_classes (int) - number of classification classes
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
      """
  
      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().__init__()
  
          # First convolution
          self.features = nn.Sequential(
              OrderedDict(
                  [
                      ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, 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)
  
      def forward(self, x: Tensor) -> Tensor:
          features = self.features(x)
          out = F.relu(features, inplace=True)
          out = F.adaptive_avg_pool2d(out, (1, 1))
          out = torch.flatten(out, 1)
          out = self.classifier(out)
          return out
  
apis: torch.nn.BatchNorm2d torch.nn.init.kaiming_normal_ torch.utils.checkpoint.checkpoint torch.nn.Linear torch.nn.MaxPool2d torch.nn.functional.dropout torch.nn.init.constant_ torch.flatten torch.nn.functional.relu torch.nn.ReLU torch.nn.functional.adaptive_avg_pool2d torch.nn.Conv2d torch.jit.is_scripting torch.nn.AvgPool2d torch.cat