text: """ Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin`
  
  Attributes:
  
      _out_channels (list of int): specify number of channels for each encoder feature tensor
      _depth (int): specify number of stages in decoder (in other words number of downsampling operations)
      _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3)
  
  Methods:
  
      forward(self, x: torch.Tensor)
          produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of
          shape NCHW (features should be sorted in descending order according to spatial resolution, starting
          with resolution same as input `x` tensor).
  
          Input: `x` with shape (1, 3, 64, 64)
          Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes
                  [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8),
                  (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ)
  
          also should support number of features according to specified depth, e.g. if depth = 5,
          number of feature tensors = 6 (one with same resolution as input and 5 downsampled),
          depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled).
  """
  
  import torch.nn as nn
  from torchvision.models.vgg import VGG
  from torchvision.models.vgg import make_layers
  from pretrainedmodels.models.torchvision_models import pretrained_settings
  
  from ._base import EncoderMixin
  from ..base.module import Module
  
  # fmt: off
  cfg = {
      'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
      'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
      'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
      'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
  }
  # fmt: on
  
  
  class VGGEncoder(VGG, EncoderMixin, Module):
      def __init__(self, out_channels, config, batch_norm=False, depth=5, **kwargs):
          super().__init__(make_layers(config, batch_norm=batch_norm), **kwargs)
          self._out_channels = out_channels
          self._depth = depth
          self._in_channels = 3
          del self.classifier
  
      def forward(self, x):
          features = []
          for module in self.features:
              if isinstance(module, nn.MaxPool2d):
                  features.append(x)
                  if len(features) == self._depth + 1:
                      break
              x = module(x)
  
          if len(features) < self._depth + 1:
              features.append(x)
  
          return features
  
      def load_state_dict(self, state_dict, **kwargs):
          keys = list(state_dict.keys())
          for k in keys:
              if k.startswith("classifier"):
                  state_dict.pop(k)
          super().load_state_dict(state_dict, **kwargs)
  
  
  vgg_encoders = {
      "vgg11": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg11"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["A"],
              "batch_norm": False,
          },
      },
      "vgg11_bn": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg11_bn"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["A"],
              "batch_norm": True,
          },
      },
      "vgg13": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg13"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["B"],
              "batch_norm": False,
          },
      },
      "vgg13_bn": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg13_bn"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["B"],
              "batch_norm": True,
          },
      },
      "vgg16": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg16"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["D"],
              "batch_norm": False,
          },
      },
      "vgg16_bn": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg16_bn"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["D"],
              "batch_norm": True,
          },
      },
      "vgg19": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg19"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["E"],
              "batch_norm": False,
          },
      },
      "vgg19_bn": {
          "encoder": VGGEncoder,
          "pretrained_settings": pretrained_settings["vgg19_bn"],
          "params": {
              "out_channels": (64, 128, 256, 512, 512, 512),
              "config": cfg["E"],
              "batch_norm": True,
          },
      },
  }
  