hunk.code_window:                  weight_decay=0, schedule_decay=4e-3):
           if not 0.0 <= lr:
               raise ValueError("Invalid learning rate: {}".format(lr))
           defaults = dict(
               lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, schedule_decay=schedule_decay)
           super(Nadam, self).__init__(params, defaults)
       @torch.no_grad()
       def step(self, closure=None):
           """Performs a single optimization step.
  
hunk.labels: keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:             lr=lr,
               betas=betas,
               eps=eps,
               weight_decay=weight_decay,
               schedule_decay=schedule_decay,
           )
  
hunk.file_path: timm/optim/nadam.py
hunk.type: replace
file: """PyTorch ResNet
  
  This started as a copy of https://github.com/pytorch/vision 'resnet.py' (BSD-3-Clause) with
  additional dropout and dynamic global avg/max pool.
  
  ResNeXt, SE-ResNeXt, SENet, and MXNet Gluon stem/downsample variants, tiered stems added by Ross Wightman
  
  Copyright 2019, Ross Wightman
  """
  import math
  from functools import partial
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, create_attn, get_attn, \
      get_act_layer, get_norm_layer, create_classifier
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
  
  __all__ = ['ResNet', 'BasicBlock', 'Bottleneck']  # model_registry will add each entrypoint fn to this
  
  
  def get_padding(kernel_size, stride, dilation=1):
      padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
      return padding
  
  
  def create_aa(aa_layer, channels, stride=2, enable=True):
      if not aa_layer or not enable:
          return nn.Identity()
      if issubclass(aa_layer, nn.AvgPool2d):
          return aa_layer(stride)
      else:
          return aa_layer(channels=channels, stride=stride)
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(
              self,
              inplanes,
              planes,
              stride=1,
              downsample=None,
              cardinality=1,
              base_width=64,
              reduce_first=1,
              dilation=1,
              first_dilation=None,
              act_layer=nn.ReLU,
              norm_layer=nn.BatchNorm2d,
              attn_layer=None,
              aa_layer=None,
              drop_block=None,
              drop_path=None,
      ):
          super(BasicBlock, self).__init__()
  
          assert cardinality == 1, 'BasicBlock only supports cardinality of 1'
          assert base_width == 64, 'BasicBlock does not support changing base width'
          first_planes = planes // reduce_first
          outplanes = planes * self.expansion
          first_dilation = first_dilation or dilation
          use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
  
          self.conv1 = nn.Conv2d(
              inplanes, first_planes, kernel_size=3, stride=1 if use_aa else stride, padding=first_dilation,
              dilation=first_dilation, bias=False)
          self.bn1 = norm_layer(first_planes)
          self.drop_block = drop_block() if drop_block is not None else nn.Identity()
          self.act1 = act_layer(inplace=True)
          self.aa = create_aa(aa_layer, channels=first_planes, stride=stride, enable=use_aa)
  
          self.conv2 = nn.Conv2d(
              first_planes, outplanes, kernel_size=3, padding=dilation, dilation=dilation, bias=False)
          self.bn2 = norm_layer(outplanes)
  
          self.se = create_attn(attn_layer, outplanes)
  
          self.act2 = act_layer(inplace=True)
          self.downsample = downsample
          self.stride = stride
          self.dilation = dilation
          self.drop_path = drop_path
  
      def zero_init_last(self):
          if getattr(self.bn2, 'weight', None) is not None:
              nn.init.zeros_(self.bn2.weight)
  
      def forward(self, x):
          shortcut = x
  
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.drop_block(x)
          x = self.act1(x)
          x = self.aa(x)
  
          x = self.conv2(x)
          x = self.bn2(x)
  
          if self.se is not None:
              x = self.se(x)
  
          if self.drop_path is not None:
              x = self.drop_path(x)
  
          if self.downsample is not None:
              shortcut = self.downsample(shortcut)
          x += shortcut
          x = self.act2(x)
  
          return x
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(
              self,
              inplanes,
              planes,
              stride=1,
              downsample=None,
              cardinality=1,
              base_width=64,
              reduce_first=1,
              dilation=1,
              first_dilation=None,
              act_layer=nn.ReLU,
              norm_layer=nn.BatchNorm2d,
              attn_layer=None,
              aa_layer=None,
              drop_block=None,
              drop_path=None,
      ):
          super(Bottleneck, self).__init__()
  
          width = int(math.floor(planes * (base_width / 64)) * cardinality)
          first_planes = width // reduce_first
          outplanes = planes * self.expansion
          first_dilation = first_dilation or dilation
          use_aa = aa_layer is not None and (stride == 2 or first_dilation != dilation)
  
          self.conv1 = nn.Conv2d(inplanes, first_planes, kernel_size=1, bias=False)
          self.bn1 = norm_layer(first_planes)
          self.act1 = act_layer(inplace=True)
  
          self.conv2 = nn.Conv2d(
              first_planes, width, kernel_size=3, stride=1 if use_aa else stride,
              padding=first_dilation, dilation=first_dilation, groups=cardinality, bias=False)
          self.bn2 = norm_layer(width)
          self.drop_block = drop_block() if drop_block is not None else nn.Identity()
          self.act2 = act_layer(inplace=True)
          self.aa = create_aa(aa_layer, channels=width, stride=stride, enable=use_aa)
  
          self.conv3 = nn.Conv2d(width, outplanes, kernel_size=1, bias=False)
          self.bn3 = norm_layer(outplanes)
  
          self.se = create_attn(attn_layer, outplanes)
  
          self.act3 = act_layer(inplace=True)
          self.downsample = downsample
          self.stride = stride
          self.dilation = dilation
          self.drop_path = drop_path
  
      def zero_init_last(self):
          if getattr(self.bn3, 'weight', None) is not None:
              nn.init.zeros_(self.bn3.weight)
  
      def forward(self, x):
          shortcut = x
  
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.act1(x)
  
          x = self.conv2(x)
          x = self.bn2(x)
          x = self.drop_block(x)
          x = self.act2(x)
          x = self.aa(x)
  
          x = self.conv3(x)
          x = self.bn3(x)
  
          if self.se is not None:
              x = self.se(x)
  
          if self.drop_path is not None:
              x = self.drop_path(x)
  
          if self.downsample is not None:
              shortcut = self.downsample(shortcut)
          x += shortcut
          x = self.act3(x)
  
          return x
  
  
  def downsample_conv(
          in_channels,
          out_channels,
          kernel_size,
          stride=1,
          dilation=1,
          first_dilation=None,
          norm_layer=None,
  ):
      norm_layer = norm_layer or nn.BatchNorm2d
      kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
      first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1
      p = get_padding(kernel_size, stride, first_dilation)
  
      return nn.Sequential(*[
          nn.Conv2d(
              in_channels, out_channels, kernel_size, stride=stride, padding=p, dilation=first_dilation, bias=False),
          norm_layer(out_channels)
      ])
  
  
  def downsample_avg(
          in_channels,
          out_channels,
          kernel_size,
          stride=1,
          dilation=1,
          first_dilation=None,
          norm_layer=None,
  ):
      norm_layer = norm_layer or nn.BatchNorm2d
      avg_stride = stride if dilation == 1 else 1
      if stride == 1 and dilation == 1:
          pool = nn.Identity()
      else:
          avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
          pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
  
      return nn.Sequential(*[
          pool,
          nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False),
          norm_layer(out_channels)
      ])
  
  
  def drop_blocks(drop_prob=0.):
      return [
          None, None,
          partial(DropBlock2d, drop_prob=drop_prob, block_size=5, gamma_scale=0.25) if drop_prob else None,
          partial(DropBlock2d, drop_prob=drop_prob, block_size=3, gamma_scale=1.00) if drop_prob else None]
  
  
  def make_blocks(
          block_fn,
          channels,
          block_repeats,
          inplanes,
          reduce_first=1,
          output_stride=32,
          down_kernel_size=1,
          avg_down=False,
          drop_block_rate=0.,
          drop_path_rate=0.,
          **kwargs,
  ):
      stages = []
      feature_info = []
      net_num_blocks = sum(block_repeats)
      net_block_idx = 0
      net_stride = 4
      dilation = prev_dilation = 1
      for stage_idx, (planes, num_blocks, db) in enumerate(zip(channels, block_repeats, drop_blocks(drop_block_rate))):
          stage_name = f'layer{stage_idx + 1}'  # never liked this name, but weight compat requires it
          stride = 1 if stage_idx == 0 else 2
          if net_stride >= output_stride:
              dilation *= stride
              stride = 1
          else:
              net_stride *= stride
  
          downsample = None
          if stride != 1 or inplanes != planes * block_fn.expansion:
              down_kwargs = dict(
                  in_channels=inplanes,
                  out_channels=planes * block_fn.expansion,
                  kernel_size=down_kernel_size,
                  stride=stride,
                  dilation=dilation,
                  first_dilation=prev_dilation,
                  norm_layer=kwargs.get('norm_layer'),
              )
              downsample = downsample_avg(**down_kwargs) if avg_down else downsample_conv(**down_kwargs)
  
          block_kwargs = dict(reduce_first=reduce_first, dilation=dilation, drop_block=db, **kwargs)
          blocks = []
          for block_idx in range(num_blocks):
              downsample = downsample if block_idx == 0 else None
              stride = stride if block_idx == 0 else 1
              block_dpr = drop_path_rate * net_block_idx / (net_num_blocks - 1)  # stochastic depth linear decay rule
              blocks.append(block_fn(
                  inplanes,
                  planes,
                  stride,
                  downsample,
                  first_dilation=prev_dilation,
                  drop_path=DropPath(block_dpr) if block_dpr > 0. else None,
                  **block_kwargs,
              ))
              prev_dilation = dilation
              inplanes = planes * block_fn.expansion
              net_block_idx += 1
  
          stages.append((stage_name, nn.Sequential(*blocks)))
          feature_info.append(dict(num_chs=inplanes, reduction=net_stride, module=stage_name))
  
      return stages, feature_info
  
  
  class ResNet(nn.Module):
      """ResNet / ResNeXt / SE-ResNeXt / SE-Net
  
      This class implements all variants of ResNet, ResNeXt, SE-ResNeXt, and SENet that
        * have > 1 stride in the 3x3 conv layer of bottleneck
        * have conv-bn-act ordering
  
      This ResNet impl supports a number of stem and downsample options based on the v1c, v1d, v1e, and v1s
      variants included in the MXNet Gluon ResNetV1b model. The C and D variants are also discussed in the
      'Bag of Tricks' paper: https://arxiv.org/pdf/1812.01187. The B variant is equivalent to torchvision default.
  
      ResNet variants (the same modifications can be used in SE/ResNeXt models as well):
        * normal, b - 7x7 stem, stem_width = 64, same as torchvision ResNet, NVIDIA ResNet 'v1.5', Gluon v1b
        * c - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64)
        * d - 3 layer deep 3x3 stem, stem_width = 32 (32, 32, 64), average pool in downsample
        * e - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128), average pool in downsample
        * s - 3 layer deep 3x3 stem, stem_width = 64 (64, 64, 128)
        * t - 3 layer deep 3x3 stem, stem width = 32 (24, 48, 64), average pool in downsample
        * tn - 3 layer deep 3x3 stem, stem width = 32 (24, 32, 64), average pool in downsample
  
      ResNeXt
        * normal - 7x7 stem, stem_width = 64, standard cardinality and base widths
        * same c,d, e, s variants as ResNet can be enabled
  
      SE-ResNeXt
        * normal - 7x7 stem, stem_width = 64
        * same c, d, e, s variants as ResNet can be enabled
  
      SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64,
          reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block
      """
  
      def __init__(
              self,
              block,
              layers,
              num_classes=1000,
              in_chans=3,
              output_stride=32,
              global_pool='avg',
              cardinality=1,
              base_width=64,
              stem_width=64,
              stem_type='',
              replace_stem_pool=False,
              block_reduce_first=1,
              down_kernel_size=1,
              avg_down=False,
              act_layer=nn.ReLU,
              norm_layer=nn.BatchNorm2d,
              aa_layer=None,
              drop_rate=0.0,
              drop_path_rate=0.,
              drop_block_rate=0.,
              zero_init_last=True,
              block_args=None,
      ):
          """
          Args:
              block (nn.Module): class for the residual block. Options are BasicBlock, Bottleneck.
              layers (List[int]) : number of layers in each block
              num_classes (int): number of classification classes (default 1000)
              in_chans (int): number of input (color) channels. (default 3)
              output_stride (int): output stride of the network, 32, 16, or 8. (default 32)
              global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg')
              cardinality (int): number of convolution groups for 3x3 conv in Bottleneck. (default 1)
              base_width (int): bottleneck channels factor. `planes * base_width / 64 * cardinality` (default 64)
              stem_width (int): number of channels in stem convolutions (default 64)
              stem_type (str): The type of stem (default ''):
                  * '', default - a single 7x7 conv with a width of stem_width
                  * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2
                  * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2
              replace_stem_pool (bool): replace stem max-pooling layer with a 3x3 stride-2 convolution
              block_reduce_first (int): Reduction factor for first convolution output width of residual blocks,
                  1 for all archs except senets, where 2 (default 1)
              down_kernel_size (int): kernel size of residual block downsample path,
                  1x1 for most, 3x3 for senets (default: 1)
              avg_down (bool): use avg pooling for projection skip connection between stages/downsample (default False)
              act_layer (str, nn.Module): activation layer
              norm_layer (str, nn.Module): normalization layer
              aa_layer (nn.Module): anti-aliasing layer
              drop_rate (float): Dropout probability before classifier, for training (default 0.)
              drop_path_rate (float): Stochastic depth drop-path rate (default 0.)
              drop_block_rate (float): Drop block rate (default 0.)
              zero_init_last (bool): zero-init the last weight in residual path (usually last BN affine weight)
              block_args (dict): Extra kwargs to pass through to block module
          """
          super(ResNet, self).__init__()
          block_args = block_args or dict()
          assert output_stride in (8, 16, 32)
          self.num_classes = num_classes
          self.drop_rate = drop_rate
          self.grad_checkpointing = False
          
          act_layer = get_act_layer(act_layer)
          norm_layer = get_norm_layer(norm_layer)
  
          # Stem
          deep_stem = 'deep' in stem_type
          inplanes = stem_width * 2 if deep_stem else 64
          if deep_stem:
              stem_chs = (stem_width, stem_width)
              if 'tiered' in stem_type:
                  stem_chs = (3 * (stem_width // 4), stem_width)
              self.conv1 = nn.Sequential(*[
                  nn.Conv2d(in_chans, stem_chs[0], 3, stride=2, padding=1, bias=False),
                  norm_layer(stem_chs[0]),
                  act_layer(inplace=True),
                  nn.Conv2d(stem_chs[0], stem_chs[1], 3, stride=1, padding=1, bias=False),
                  norm_layer(stem_chs[1]),
                  act_layer(inplace=True),
                  nn.Conv2d(stem_chs[1], inplanes, 3, stride=1, padding=1, bias=False)])
          else:
              self.conv1 = nn.Conv2d(in_chans, inplanes, kernel_size=7, stride=2, padding=3, bias=False)
          self.bn1 = norm_layer(inplanes)
          self.act1 = act_layer(inplace=True)
          self.feature_info = [dict(num_chs=inplanes, reduction=2, module='act1')]
  
          # Stem pooling. The name 'maxpool' remains for weight compatibility.
          if replace_stem_pool:
              self.maxpool = nn.Sequential(*filter(None, [
                  nn.Conv2d(inplanes, inplanes, 3, stride=1 if aa_layer else 2, padding=1, bias=False),
                  create_aa(aa_layer, channels=inplanes, stride=2) if aa_layer is not None else None,
                  norm_layer(inplanes),
                  act_layer(inplace=True),
              ]))
          else:
              if aa_layer is not None:
                  if issubclass(aa_layer, nn.AvgPool2d):
                      self.maxpool = aa_layer(2)
                  else:
                      self.maxpool = nn.Sequential(*[
                          nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
                          aa_layer(channels=inplanes, stride=2)])
              else:
                  self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  
          # Feature Blocks
          channels = [64, 128, 256, 512]
          stage_modules, stage_feature_info = make_blocks(
              block,
              channels,
              layers,
              inplanes,
              cardinality=cardinality,
              base_width=base_width,
              output_stride=output_stride,
              reduce_first=block_reduce_first,
              avg_down=avg_down,
              down_kernel_size=down_kernel_size,
              act_layer=act_layer,
              norm_layer=norm_layer,
              aa_layer=aa_layer,
              drop_block_rate=drop_block_rate,
              drop_path_rate=drop_path_rate,
              **block_args,
          )
          for stage in stage_modules:
              self.add_module(*stage)  # layer1, layer2, etc
          self.feature_info.extend(stage_feature_info)
  
          # Head (Pooling and Classifier)
          self.num_features = 512 * block.expansion
          self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
  
          self.init_weights(zero_init_last=zero_init_last)
  
      @torch.jit.ignore
      def init_weights(self, zero_init_last=True):
          for n, m in self.named_modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
          if zero_init_last:
              for m in self.modules():
                  if hasattr(m, 'zero_init_last'):
                      m.zero_init_last()
  
      @torch.jit.ignore
      def group_matcher(self, coarse=False):
          matcher = dict(stem=r'^conv1|bn1|maxpool', blocks=r'^layer(\d+)' if coarse else r'^layer(\d+)\.(\d+)')
          return matcher
  
      @torch.jit.ignore
      def set_grad_checkpointing(self, enable=True):
          self.grad_checkpointing = enable
  
      @torch.jit.ignore
      def get_classifier(self, name_only=False):
          return 'fc' if name_only else self.fc
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.num_classes = num_classes
          self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
  
      def forward_features(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.act1(x)
          x = self.maxpool(x)
  
          if self.grad_checkpointing and not torch.jit.is_scripting():
              x = checkpoint_seq([self.layer1, self.layer2, self.layer3, self.layer4], x, flatten=True)
          else:
              x = self.layer1(x)
              x = self.layer2(x)
              x = self.layer3(x)
              x = self.layer4(x)
          return x
  
      def forward_head(self, x, pre_logits: bool = False):
          x = self.global_pool(x)
          if self.drop_rate:
              x = F.dropout(x, p=float(self.drop_rate), training=self.training)
          return x if pre_logits else self.fc(x)
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.forward_head(x)
          return x
  
  
  def _create_resnet(variant, pretrained=False, **kwargs):
      return build_model_with_cfg(ResNet, variant, pretrained, **kwargs)
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
          'crop_pct': 0.875, 'interpolation': 'bilinear',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'conv1', 'classifier': 'fc',
          **kwargs
      }
  
  
  def _tcfg(url='', **kwargs):
      return _cfg(url=url, **dict({'interpolation': 'bicubic'}, **kwargs))
  
  
  def _ttcfg(url='', **kwargs):
      return _cfg(url=url, **dict({
          'interpolation': 'bicubic', 'test_input_size': (3, 288, 288), 'test_crop_pct': 0.95,
          'origin_url': 'https://github.com/huggingface/pytorch-image-models',
      }, **kwargs))
  
  
  def _rcfg(url='', **kwargs):
      return _cfg(url=url, **dict({
          'interpolation': 'bicubic', 'crop_pct': 0.95, 'test_input_size': (3, 288, 288), 'test_crop_pct': 1.0,
          'origin_url': 'https://github.com/huggingface/pytorch-image-models', 'paper_ids': 'arXiv:2110.00476'
      }, **kwargs))
  
  
  def _r3cfg(url='', **kwargs):
      return _cfg(url=url, **dict({
          'interpolation': 'bicubic', 'input_size': (3, 160, 160), 'pool_size': (5, 5),
          'crop_pct': 0.95, 'test_input_size': (3, 224, 224), 'test_crop_pct': 0.95,
          'origin_url': 'https://github.com/huggingface/pytorch-image-models', 'paper_ids': 'arXiv:2110.00476',
      }, **kwargs))
  
  
  def _gcfg(url='', **kwargs):
      return _cfg(url=url, **dict({
          'interpolation': 'bicubic',
          'origin_url': 'https://cv.gluon.ai/model_zoo/classification.html',
      }, **kwargs))
  
  
  default_cfgs = generate_default_cfgs({
      # ResNet and Wide ResNet trained w/ timm (RSB paper and others)
      'resnet10t.c3_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet10t_176_c3-f3215ab1.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_crop_pct=0.95, test_input_size=(3, 224, 224),
          first_conv='conv1.0'),
      'resnet14t.c3_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet14t_176_c3-c4ed2c37.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_crop_pct=0.95, test_input_size=(3, 224, 224),
          first_conv='conv1.0'),
      'resnet18.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet18_a1_0-d63eafa0.pth'),
      'resnet18.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet18_a2_0-b61bd467.pth'),
      'resnet18.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet18_a3_0-40c531c8.pth'),
      'resnet18d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet18d_ra2-48a79e06.pth',
          first_conv='conv1.0'),
      'resnet34.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet34_a1_0-46f8f793.pth'),
      'resnet34.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet34_a2_0-82d47d71.pth'),
      'resnet34.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet34_a3_0-a20cabb6.pth',
          crop_pct=0.95),
      'resnet34.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth'),
      'resnet34d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34d_ra2-f8dcfcaf.pth',
          first_conv='conv1.0'),
      'resnet26.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26-9aa10e23.pth'),
      'resnet26d.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet26d-69e92c46.pth',
          first_conv='conv1.0'),
      'resnet26t.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-attn-weights/resnet26t_256_ra2-6f6fa748.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
          crop_pct=0.94, test_input_size=(3, 320, 320), test_crop_pct=1.0),
      'resnet50.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth'),
      'resnet50.a1h_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), crop_pct=0.9, test_input_size=(3, 224, 224), test_crop_pct=1.0),
      'resnet50.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a2_0-a2746f79.pth'),
      'resnet50.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a3_0-59cae1ef.pth'),
      'resnet50.b1k_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_b1k-532a802a.pth'),
      'resnet50.b2k_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_b2k-1ba180c1.pth'),
      'resnet50.c1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_c1-5ba5e060.pth'),
      'resnet50.c2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_c2-d01e05b2.pth'),
      'resnet50.d_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_d-f39db8af.pth'),
      'resnet50.ram_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnet50_ram-a26f946b.pth'),
      'resnet50.am_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnet50_am-6c502b37.pth'),
      'resnet50.ra_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnet50_ra-85ebb6e5.pth'),
      'resnet50.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/rw_resnet50-86acaeed.pth'),
      'resnet50d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet50d_ra2-464e36ba.pth',
          first_conv='conv1.0'),
      'resnet50d.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50d_a1_0-e20cff14.pth',
          first_conv='conv1.0'),
      'resnet50d.a2_in1k': _rcfg(
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50d_a2_0-a3adc64d.pth',
          first_conv='conv1.0'),
      'resnet50d.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50d_a3_0-403fdfad.pth',
          first_conv='conv1.0'),
      'resnet50t.untrained': _ttcfg(first_conv='conv1.0'),
      'resnet101.a1h_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a1h-36d3f2aa.pth'),
      'resnet101.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a1_0-cdcb52a9.pth'),
      'resnet101.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a2_0-6edb36c7.pth'),
      'resnet101.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a3_0-1db14157.pth'),
      'resnet101d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet101d_ra2-2803ffab.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
          test_crop_pct=1.0, test_input_size=(3, 320, 320)),
      'resnet152.a1h_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a1h-dc400468.pth'),
      'resnet152.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a1_0-2eee8a7a.pth'),
      'resnet152.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a2_0-b4c6978f.pth'),
      'resnet152.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet152_a3_0-134d4688.pth'),
      'resnet152d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet152d_ra2-5cac0439.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
          test_crop_pct=1.0, test_input_size=(3, 320, 320)),
      'resnet200.untrained': _ttcfg(),
      'resnet200d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet200d_ra2-bdba9bf9.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
          test_crop_pct=1.0, test_input_size=(3, 320, 320)),
      'wide_resnet50_2.racm_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth'),
  
      # torchvision resnet weights
      'resnet18.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet18-5c106cde.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet34.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet34-333f7ec4.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet50.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet50-19c8e357.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet50.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet50-11ad3fa6.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet101.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet101.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet101-cd907fc2.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet152.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet152-b121ed2d.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnet152.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnet152-f82ba261.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'wide_resnet50_2.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'wide_resnet50_2.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'wide_resnet101_2.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'wide_resnet101_2.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
  
      # ResNets w/ alternative norm layers
      'resnet50_gn.a1h_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_gn_a1h2-8fe6c4d0.pth',
          crop_pct=0.94),
  
      # ResNeXt trained in timm (RSB paper and others)
      'resnext50_32x4d.a1h_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a1h-0146ab0a.pth'),
      'resnext50_32x4d.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a1_0-b5a91a1d.pth'),
      'resnext50_32x4d.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a2_0-efc76add.pth'),
      'resnext50_32x4d.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/resnext50_32x4d_a3_0-3e450271.pth'),
      'resnext50_32x4d.ra_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-weights/resnext50_32x4d_ra-d733960d.pth'),
      'resnext50d_32x4d.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnext50d_32x4d-103e99f8.pth',
          first_conv='conv1.0'),
      'resnext101_32x4d.untrained': _ttcfg(),
      'resnext101_64x4d.c1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnext101_64x4d_c-0d0e0cc0.pth'),
  
      # torchvision ResNeXt weights
      'resnext50_32x4d.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnext101_32x8d.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnext101_64x4d.tv_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth',
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnext50_32x4d.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
      'resnext101_32x8d.tv2_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth',
          input_size=(3, 176, 176), pool_size=(6, 6), test_input_size=(3, 224, 224), test_crop_pct=0.965,
          license='bsd-3-clause', origin_url='https://github.com/pytorch/vision'),
  
      #  ResNeXt models - Weakly Supervised Pretraining on Instagram Hashtags
      #  from https://github.com/facebookresearch/WSL-Images
      #  Please note the CC-BY-NC 4.0 license on these weights, non-commercial use only.
      'resnext101_32x8d.fb_wsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/ig_resnext101_32x8-c38310e5.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
      'resnext101_32x16d.fb_wsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
      'resnext101_32x32d.fb_wsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
      'resnext101_32x48d.fb_wsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/WSL-Images'),
  
      #  Semi-Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
      #  Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
      'resnet18.fb_ssl_yfcc100m_ft_in1k':  _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnet50.fb_ssl_yfcc100m_ft_in1k':  _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
  
      #  Semi-Weakly Supervised ResNe*t models from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models
      #  Please note the CC-BY-NC 4.0 license on theses weights, non-commercial use only.
      'resnet18.fb_swsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnet50.fb_swsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext50_32x4d.fb_swsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext101_32x4d.fb_swsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext101_32x8d.fb_swsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
      'resnext101_32x16d.fb_swsl_ig1b_ft_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth',
          license='cc-by-nc-4.0', origin_url='https://github.com/facebookresearch/semi-supervised-ImageNet1K-models'),
  
      #  Efficient Channel Attention ResNets
      'ecaresnet26t.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet26t_ra2-46609757.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
          test_crop_pct=0.95, test_input_size=(3, 320, 320)),
      'ecaresnetlight.miil_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnetlight-75a9c627.pth',
          test_crop_pct=0.95, test_input_size=(3, 288, 288)),
      'ecaresnet50d.miil_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d-93c81e3b.pth',
          first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
      'ecaresnet50d_pruned.miil_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet50d_p-e4fa23c2.pth',
          first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
      'ecaresnet50t.ra2_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet50t_ra2-f7ac63c4.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8),
          test_crop_pct=0.95, test_input_size=(3, 320, 320)),
      'ecaresnet50t.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/ecaresnet50t_a1_0-99bd76a8.pth',
          first_conv='conv1.0'),
      'ecaresnet50t.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/ecaresnet50t_a2_0-b1c7b745.pth',
          first_conv='conv1.0'),
      'ecaresnet50t.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/ecaresnet50t_a3_0-8cc311f1.pth',
          first_conv='conv1.0'),
      'ecaresnet101d.miil_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d-153dad65.pth',
          first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
      'ecaresnet101d_pruned.miil_in1k': _tcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/ecaresnet101d_p-9e74cb91.pth',
          first_conv='conv1.0', test_crop_pct=0.95, test_input_size=(3, 288, 288)),
      'ecaresnet200d.untrained': _ttcfg(
          first_conv='conv1.0', input_size=(3, 256, 256), crop_pct=0.95, pool_size=(8, 8)),
      'ecaresnet269d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecaresnet269d_320_ra2-7baa55cb.pth',
          first_conv='conv1.0', input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=0.95,
          test_crop_pct=1.0, test_input_size=(3, 352, 352)),
  
      #  Efficient Channel Attention ResNeXts
      'ecaresnext26t_32x4d.untrained': _tcfg(first_conv='conv1.0'),
      'ecaresnext50t_32x4d.untrained': _tcfg(first_conv='conv1.0'),
  
      #  Squeeze-Excitation ResNets, to eventually replace the models in senet.py
      'seresnet18.untrained': _ttcfg(),
      'seresnet34.untrained': _ttcfg(),
      'seresnet50.a1_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/seresnet50_a1_0-ffa00869.pth',
          crop_pct=0.95),
      'seresnet50.a2_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/seresnet50_a2_0-850de0d9.pth',
          crop_pct=0.95),
      'seresnet50.a3_in1k': _r3cfg(
          hf_hub_id='timm/',
          url='https://github.com/huggingface/pytorch-image-models/releases/download/v0.1-rsb-weights/seresnet50_a3_0-317ecd56.pth',
          crop_pct=0.95),
      'seresnet50.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth'),
      'seresnet50t.untrained': _ttcfg(
          first_conv='conv1.0'),
      'seresnet101.untrained': _ttcfg(),
      'seresnet152.untrained': _ttcfg(),
      'seresnet152d.ra2_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth',
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=0.95,
          test_crop_pct=1.0, test_input_size=(3, 320, 320)
      ),
      'seresnet200d.untrained': _ttcfg(
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
      'seresnet269d.untrained': _ttcfg(
          first_conv='conv1.0', input_size=(3, 256, 256), pool_size=(8, 8)),
  
      #  Squeeze-Excitation ResNeXts, to eventually replace the models in senet.py
      'seresnext26d_32x4d.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26d_32x4d-80fa48a3.pth',
          first_conv='conv1.0'),
      'seresnext26t_32x4d.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26tn_32x4d-569cb627.pth',
          first_conv='conv1.0'),
      'seresnext50_32x4d.racm_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext50_32x4d_racm-a304a460.pth'),
      'seresnext101_32x4d.untrained': _ttcfg(),
      'seresnext101_32x8d.ah_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101_32x8d_ah-e6bc4c0a.pth'),
      'seresnext101d_32x8d.ah_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnext101d_32x8d_ah-191d7b94.pth',
          first_conv='conv1.0'),
  
      # ResNets with anti-aliasing / blur pool
      'resnetaa50d.sw_in12k_ft_in1k': _ttcfg(
          hf_hub_id='timm/',
          first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
      'resnetaa101d.sw_in12k_ft_in1k': _ttcfg(
          hf_hub_id='timm/',
          first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
      'seresnextaa101d_32x8d.sw_in12k_ft_in1k_288': _ttcfg(
          hf_hub_id='timm/',
          crop_pct=0.95, input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), test_crop_pct=1.0,
          first_conv='conv1.0'),
      'seresnextaa101d_32x8d.sw_in12k_ft_in1k': _ttcfg(
          hf_hub_id='timm/',
          first_conv='conv1.0', test_crop_pct=1.0),
  
      'resnetaa50d.sw_in12k': _ttcfg(
          hf_hub_id='timm/',
          num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
      'resnetaa50d.d_in12k': _ttcfg(
          hf_hub_id='timm/',
          num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
      'resnetaa101d.sw_in12k': _ttcfg(
          hf_hub_id='timm/',
          num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
      'seresnextaa101d_32x8d.sw_in12k': _ttcfg(
          hf_hub_id='timm/',
          num_classes=11821, first_conv='conv1.0', crop_pct=0.95, test_crop_pct=1.0),
  
      'resnetblur18.untrained': _ttcfg(),
      'resnetblur50.bt_in1k': _ttcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnetblur50-84f4748f.pth'),
      'resnetblur50d.untrained': _ttcfg(first_conv='conv1.0'),
      'resnetblur101d.untrained': _ttcfg(first_conv='conv1.0'),
      'resnetaa50.a1h_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnetaa50_a1h-4cf422b3.pth'),
  
      'seresnetaa50d.untrained': _ttcfg(first_conv='conv1.0'),
      'seresnextaa101d_32x8d.ah_in1k': _rcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/seresnextaa101d_32x8d_ah-83c8ae12.pth',
          first_conv='conv1.0'),
  
      # ResNet-RS models
      'resnetrs50.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs50_ema-6b53758b.pth',
          input_size=(3, 160, 160), pool_size=(5, 5), crop_pct=0.91, test_input_size=(3, 224, 224),
          interpolation='bicubic', first_conv='conv1.0'),
      'resnetrs101.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs101_i192_ema-1509bbf6.pth',
          input_size=(3, 192, 192), pool_size=(6, 6), crop_pct=0.94, test_input_size=(3, 288, 288),
          interpolation='bicubic', first_conv='conv1.0'),
      'resnetrs152.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs152_i256_ema-a9aff7f9.pth',
          input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
          interpolation='bicubic', first_conv='conv1.0'),
      'resnetrs200.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/resnetrs200_c-6b698b88.pth',
          input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 320, 320),
          interpolation='bicubic', first_conv='conv1.0'),
      'resnetrs270.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs270_ema-b40e674c.pth',
          input_size=(3, 256, 256), pool_size=(8, 8), crop_pct=1.0, test_input_size=(3, 352, 352),
          interpolation='bicubic', first_conv='conv1.0'),
      'resnetrs350.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs350_i256_ema-5a1aa8f1.pth',
          input_size=(3, 288, 288), pool_size=(9, 9), crop_pct=1.0, test_input_size=(3, 384, 384),
          interpolation='bicubic', first_conv='conv1.0'),
      'resnetrs420.tf_in1k': _cfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rs-weights/resnetrs420_ema-972dee69.pth',
          input_size=(3, 320, 320), pool_size=(10, 10), crop_pct=1.0, test_input_size=(3, 416, 416),
          interpolation='bicubic', first_conv='conv1.0'),
  
      # gluon resnet weights
      'resnet18.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet18_v1b-0757602b.pth'),
      'resnet34.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet34_v1b-c6d82d59.pth'),
      'resnet50.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1b-0ebe02e2.pth'),
      'resnet101.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1b-3b017079.pth'),
      'resnet152.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1b-c1edb0dd.pth'),
      'resnet50c.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1c-48092f55.pth',
          first_conv='conv1.0'),
      'resnet101c.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1c-1f26822a.pth',
          first_conv='conv1.0'),
      'resnet152c.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1c-a3bb0b98.pth',
          first_conv='conv1.0'),
      'resnet50d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1d-818a1b1b.pth',
          first_conv='conv1.0'),
      'resnet101d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1d-0f9c8644.pth',
          first_conv='conv1.0'),
      'resnet152d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1d-bd354e12.pth',
          first_conv='conv1.0'),
      'resnet50s.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet50_v1s-1762acc0.pth',
          first_conv='conv1.0'),
      'resnet101s.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet101_v1s-60fe0cc1.pth',
          first_conv='conv1.0'),
      'resnet152s.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnet152_v1s-dcc41b81.pth',
          first_conv='conv1.0'),
      'resnext50_32x4d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext50_32x4d-e6a097c1.pth'),
      'resnext101_32x4d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_32x4d-b253c8c4.pth'),
      'resnext101_64x4d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_resnext101_64x4d-f9a8e184.pth'),
      'seresnext50_32x4d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext50_32x4d-90cf2d6e.pth'),
      'seresnext101_32x4d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_32x4d-cf52900d.pth'),
      'seresnext101_64x4d.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_seresnext101_64x4d-f9926f93.pth'),
      'senet154.gluon_in1k': _gcfg(
          hf_hub_id='timm/',
          url='https://github.com/rwightman/pytorch-pretrained-gluonresnet/releases/download/v0.1/gluon_senet154-70a1a3c0.pth',
          first_conv='conv1.0'),
  })
  
  
  @register_model
  def resnet10t(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-10-T model.
      """
      model_args = dict(block=BasicBlock, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True)
      return _create_resnet('resnet10t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet14t(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-14-T model.
      """
      model_args = dict(block=Bottleneck, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True)
      return _create_resnet('resnet14t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet18(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-18 model.
      """
      model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2])
      return _create_resnet('resnet18', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet18d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-18-D model.
      """
      model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet18d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet34(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-34 model.
      """
      model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3])
      return _create_resnet('resnet34', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet34d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-34-D model.
      """
      model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet34d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet26(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-26 model.
      """
      model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2])
      return _create_resnet('resnet26', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet26t(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-26-T model.
      """
      model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep_tiered', avg_down=True)
      return _create_resnet('resnet26t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet26d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-26-D model.
      """
      model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet26d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet50(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50 model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3],  **kwargs)
      return _create_resnet('resnet50', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet50c(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-C model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep')
      return _create_resnet('resnet50c', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet50d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-D model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet50d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet50s(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-S model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=64, stem_type='deep')
      return _create_resnet('resnet50s', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet50t(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-T model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True)
      return _create_resnet('resnet50t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet101(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101 model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3])
      return _create_resnet('resnet101', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet101c(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-C model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep')
      return _create_resnet('resnet101c', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet101d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-D model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet101d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet101s(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-S model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=64, stem_type='deep')
      return _create_resnet('resnet101s', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet152(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-152 model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3])
      return _create_resnet('resnet152', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet152c(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-152-C model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep')
      return _create_resnet('resnet152c', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet152d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-152-D model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet152d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet152s(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-152-S model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=64, stem_type='deep')
      return _create_resnet('resnet152s', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet200(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-200 model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3])
      return _create_resnet('resnet200', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet200d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-200-D model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnet200d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def wide_resnet50_2(pretrained=False, **kwargs) -> ResNet:
      """Constructs a Wide ResNet-50-2 model.
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
      channels, and in Wide ResNet-50-2 has 2048-1024-2048.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], base_width=128)
      return _create_resnet('wide_resnet50_2', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def wide_resnet101_2(pretrained=False, **kwargs) -> ResNet:
      """Constructs a Wide ResNet-101-2 model.
      The model is the same as ResNet except for the bottleneck number of channels
      which is twice larger in every block. The number of channels in outer 1x1
      convolutions is the same.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], base_width=128)
      return _create_resnet('wide_resnet101_2', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnet50_gn(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50 model w/ GroupNorm
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3],  **kwargs)
      return _create_resnet('resnet50_gn', pretrained, norm_layer=GroupNorm, **model_args)
  
  
  @register_model
  def resnext50_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt50-32x4d model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4)
      return _create_resnet('resnext50_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnext50d_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt50d-32x4d model. ResNext50 w/ deep stem & avg pool downsample
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3],  cardinality=32, base_width=4,
          stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnext50d_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnext101_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt-101 32x4d model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4)
      return _create_resnet('resnext101_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnext101_32x8d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt-101 32x8d model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8)
      return _create_resnet('resnext101_32x8d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnext101_32x16d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt-101 32x16d model
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16)
      return _create_resnet('resnext101_32x16d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnext101_32x32d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt-101 32x32d model
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=32)
      return _create_resnet('resnext101_32x32d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnext101_64x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNeXt101-64x4d model.
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4)
      return _create_resnet('resnext101_64x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet26t(pretrained=False, **kwargs) -> ResNet:
      """Constructs an ECA-ResNeXt-26-T model.
      This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
      in the deep stem and ECA attn.
      """
      model_args = dict(
          block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32,
          stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet26t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet50d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-D model with eca.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet50d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet50d_pruned(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-D model pruned with eca.
          The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet50t(pretrained=False, **kwargs) -> ResNet:
      """Constructs an ECA-ResNet-50-T model.
      Like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels in the deep stem and ECA attn.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32,
          stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet50t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnetlight(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-D light model with eca.
      """
      model_args = dict(
          block=Bottleneck, layers=[1, 1, 11, 3], stem_width=32, avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnetlight', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet101d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-D model with eca.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet101d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet101d_pruned(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-D model pruned with eca.
         The pruning has been obtained using https://arxiv.org/pdf/2002.08258.pdf
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet101d_pruned', pretrained, pruned=True, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet200d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-200-D model with ECA.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet200d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnet269d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-269-D model with ECA.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnet269d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnext26t_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs an ECA-ResNeXt-26-T model.
      This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
      in the deep stem. This model replaces SE module with the ECA module
      """
      model_args = dict(
          block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
          stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnext26t_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def ecaresnext50t_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs an ECA-ResNeXt-50-T model.
      This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
      in the deep stem. This model replaces SE module with the ECA module
      """
      model_args = dict(
          block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
          stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'))
      return _create_resnet('ecaresnext50t_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet18(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet18', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet34(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet34', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet50(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet50', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet50t(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3],  stem_width=32, stem_type='deep_tiered',
          avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet50t', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet101(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet101', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet152(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet152', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet152d(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep',
          avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet152d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet200d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-200-D model with SE attn.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep',
          avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet200d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnet269d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-269-D model with SE attn.
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep',
          avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnet269d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext26d_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a SE-ResNeXt-26-D model.`
      This is technically a 28 layer ResNet, using the 'D' modifier from Gluon / bag-of-tricks for
      combination of deep stem and avg_pool in downsample.
      """
      model_args = dict(
          block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
          stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext26d_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext26t_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a SE-ResNet-26-T model.
      This is technically a 28 layer ResNet, like a 'D' bag-of-tricks model but with tiered 24, 32, 64 channels
      in the deep stem.
      """
      model_args = dict(
          block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32,
          stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext26t_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext26tn_32x4d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a SE-ResNeXt-26-T model.
      NOTE I deprecated previous 't' model defs and replaced 't' with 'tn', this was the only tn model of note
      so keeping this def for backwards compat with any uses out there. Old 't' model is lost.
      """
      return seresnext26t_32x4d(pretrained=pretrained, **kwargs)
  
  
  @register_model
  def seresnext50_32x4d(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4,
          block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext50_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext101_32x4d(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4,
          block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext101_32x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext101_32x8d(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
          block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext101_32x8d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext101d_32x8d(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
          stem_width=32, stem_type='deep', avg_down=True,
          block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext101d_32x8d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnext101_64x4d(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4,
          block_args=dict(attn_layer='se'))
      return _create_resnet('seresnext101_64x4d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def senet154(pretrained=False, **kwargs) -> ResNet:
      model_args = dict(
          block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep',
          down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'))
      return _create_resnet('senet154', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetblur18(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-18 model with blur anti-aliasing
      """
      model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], aa_layer=BlurPool2d)
      return _create_resnet('resnetblur18', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetblur50(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50 model with blur anti-aliasing
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d)
      return _create_resnet('resnetblur50', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetblur50d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-D model with blur anti-aliasing
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d,
          stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnetblur50d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetblur101d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-D model with blur anti-aliasing
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=BlurPool2d,
          stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnetblur101d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetaa34d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-34-D model w/ avgpool anti-aliasing
      """
      model_args = dict(
          block=BasicBlock, layers=[3, 4, 6, 3],  aa_layer=nn.AvgPool2d, stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnetaa34d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetaa50(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50 model with avgpool anti-aliasing
      """
      model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d)
      return _create_resnet('resnetaa50', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetaa50d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-50-D model with avgpool anti-aliasing
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d,
          stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnetaa50d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetaa101d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-101-D model with avgpool anti-aliasing
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=nn.AvgPool2d,
          stem_width=32, stem_type='deep', avg_down=True)
      return _create_resnet('resnetaa101d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnetaa50d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a SE=ResNet-50-D model with avgpool anti-aliasing
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d,
          stem_width=32, stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'))
      return _create_resnet('seresnetaa50d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def seresnextaa101d_32x8d(pretrained=False, **kwargs) -> ResNet:
      """Constructs a SE=ResNeXt-101-D 32x8d model with avgpool anti-aliasing
      """
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8,
          stem_width=32, stem_type='deep', avg_down=True, aa_layer=nn.AvgPool2d,
          block_args=dict(attn_layer='se'))
      return _create_resnet('seresnextaa101d_32x8d', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetrs50(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-50 model.
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs50', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetrs101(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-101 model.
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs101', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetrs152(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-152 model.
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs152', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetrs200(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-200 model.
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs200', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetrs270(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-270 model.
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs270', pretrained, **dict(model_args, **kwargs))
  
  
  
  @register_model
  def resnetrs350(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-350 model.
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs350', pretrained, **dict(model_args, **kwargs))
  
  
  @register_model
  def resnetrs420(pretrained=False, **kwargs) -> ResNet:
      """Constructs a ResNet-RS-420 model
      Paper: Revisiting ResNets - https://arxiv.org/abs/2103.07579
      Pretrained weights from https://github.com/tensorflow/tpu/tree/bee9c4f6/models/official/resnet/resnet_rs
      """
      attn_layer = partial(get_attn('se'), rd_ratio=0.25)
      model_args = dict(
          block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_pool=True,
          avg_down=True,  block_args=dict(attn_layer=attn_layer))
      return _create_resnet('resnetrs420', pretrained, **dict(model_args, **kwargs))
  
  
  register_model_deprecations(__name__, {
      'tv_resnet34': 'resnet34.tv_in1k',
      'tv_resnet50': 'resnet50.tv_in1k',
      'tv_resnet101': 'resnet101.tv_in1k',
      'tv_resnet152': 'resnet152.tv_in1k',
      'tv_resnext50_32x4d' : 'resnext50_32x4d.tv_in1k',
      'ig_resnext101_32x8d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
      'ig_resnext101_32x16d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
      'ig_resnext101_32x32d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
      'ig_resnext101_32x48d': 'resnext101_32x8d.fb_wsl_ig1b_ft_in1k',
      'ssl_resnet18': 'resnet18.fb_ssl_yfcc100m_ft_in1k',
      'ssl_resnet50': 'resnet50.fb_ssl_yfcc100m_ft_in1k',
      'ssl_resnext50_32x4d': 'resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k',
      'ssl_resnext101_32x4d': 'resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k',
      'ssl_resnext101_32x8d': 'resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k',
      'ssl_resnext101_32x16d': 'resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k',
      'swsl_resnet18': 'resnet18.fb_swsl_ig1b_ft_in1k',
      'swsl_resnet50': 'resnet50.fb_swsl_ig1b_ft_in1k',
      'swsl_resnext50_32x4d': 'resnext50_32x4d.fb_swsl_ig1b_ft_in1k',
      'swsl_resnext101_32x4d': 'resnext101_32x4d.fb_swsl_ig1b_ft_in1k',
      'swsl_resnext101_32x8d': 'resnext101_32x8d.fb_swsl_ig1b_ft_in1k',
      'swsl_resnext101_32x16d': 'resnext101_32x16d.fb_swsl_ig1b_ft_in1k',
      'gluon_resnet18_v1b': 'resnet18.gluon_in1k',
      'gluon_resnet34_v1b': 'resnet34.gluon_in1k',
      'gluon_resnet50_v1b': 'resnet50.gluon_in1k',
      'gluon_resnet101_v1b': 'resnet101.gluon_in1k',
      'gluon_resnet152_v1b': 'resnet152.gluon_in1k',
      'gluon_resnet50_v1c': 'resnet50c.gluon_in1k',
      'gluon_resnet101_v1c': 'resnet101c.gluon_in1k',
      'gluon_resnet152_v1c': 'resnet152c.gluon_in1k',
      'gluon_resnet50_v1d': 'resnet50d.gluon_in1k',
      'gluon_resnet101_v1d': 'resnet101d.gluon_in1k',
      'gluon_resnet152_v1d': 'resnet152d.gluon_in1k',
      'gluon_resnet50_v1s': 'resnet50s.gluon_in1k',
      'gluon_resnet101_v1s': 'resnet101s.gluon_in1k',
      'gluon_resnet152_v1s': 'resnet152s.gluon_in1k',
      'gluon_resnext50_32x4d': 'resnext50_32x4d.gluon_in1k',
      'gluon_resnext101_32x4d': 'resnext101_32x4d.gluon_in1k',
      'gluon_resnext101_64x4d': 'resnext101_64x4d.gluon_in1k',
      'gluon_seresnext50_32x4d': 'seresnext50_32x4d.gluon_in1k',
      'gluon_seresnext101_32x4d': 'seresnext101_32x4d.gluon_in1k',
      'gluon_seresnext101_64x4d': 'seresnext101_64x4d.gluon_in1k',
      'gluon_senet154': 'senet154.gluon_in1k',
  })
  