code: from typing import Union, List
  
  import torch
  from torch import nn as nn
  from torch.nn import functional as F
  
  from models.layers.create_act import get_act_layer
  from .trace_utils import _assert
  
  
  class BatchNormAct2d(nn.BatchNorm2d):
      """BatchNorm + Activation
      This module performs BatchNorm + Activation in a manner that will remain backwards
      compatible with weights trained with separate bn, act. This is why we inherit from BN
      instead of composing it as a .bn member.
      """
  
      def __init__(
              self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True,
              apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None):
          super(BatchNormAct2d, self).__init__(
              num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
          self.drop = drop_layer() if drop_layer is not None else nn.Identity()
          act_layer = get_act_layer(act_layer)  # string -> nn.Module
          if act_layer is not None and apply_act:
              act_args = dict(inplace=True) if inplace else {}
              self.act = act_layer(**act_args)
          else:
              self.act = nn.Identity()
  
      def forward(self, x):
          # cut & paste of torch.nn.BatchNorm2d.forward impl to avoid issues with torchscript and tracing
          _assert(x.ndim == 4, f'expected 4D input (got {x.ndim}D input)')
  
          # exponential_average_factor is set to self.momentum
          # (when it is available) only so that it gets updated
          # in ONNX graph when this node is exported to ONNX.
          if self.momentum is None:
              exponential_average_factor = 0.0
          else:
              exponential_average_factor = self.momentum
  
          if self.training and self.track_running_stats:
              # TODO: if statement only here to tell the jit to skip emitting this when it is None
              if self.num_batches_tracked is not None:  # type: ignore[has-type]
                  self.num_batches_tracked = self.num_batches_tracked + \
                      1  # type: ignore[has-type]
                  if self.momentum is None:  # use cumulative moving average
                      exponential_average_factor = 1.0 / \
                          float(self.num_batches_tracked)
                  else:  # use exponential moving average
                      exponential_average_factor = self.momentum
  
          r"""
          Decide whether the mini-batch stats should be used for normalization rather than the buffers.
          Mini-batch stats are used in training mode, and in eval mode when buffers are None.
          """
          if self.training:
              bn_training = True
          else:
              bn_training = (self.running_mean is None) and (
                  self.running_var is None)
  
          r"""
          Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
          passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
          used for normalization (i.e. in eval mode when buffers are not None).
          """
          x = F.batch_norm(
              x,
              # If buffers are not to be tracked, ensure that they won't be updated
              self.running_mean if not self.training or self.track_running_stats else None,
              self.running_var if not self.training or self.track_running_stats else None,
              self.weight,
              self.bias,
              bn_training,
              exponential_average_factor,
              self.eps,
          )
          x = self.drop(x)
          x = self.act(x)
          return x
  
  
  def _num_groups(num_channels, num_groups, group_size):
      if group_size:
          assert num_channels % group_size == 0
          return num_channels // group_size
      return num_groups
  
  
  class GroupNormAct(nn.GroupNorm):
      # NOTE num_channel and num_groups order flipped for easier layer swaps / binding of fixed args
      def __init__(
              self, num_channels, num_groups=32, eps=1e-5, affine=True, group_size=None,
              apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None):
          super(GroupNormAct, self).__init__(
              _num_groups(num_channels, num_groups, group_size), num_channels, eps=eps, affine=affine)
          self.drop = drop_layer() if drop_layer is not None else nn.Identity()
          act_layer = get_act_layer(act_layer)  # string -> nn.Module
          if act_layer is not None and apply_act:
              act_args = dict(inplace=True) if inplace else {}
              self.act = act_layer(**act_args)
          else:
              self.act = nn.Identity()
  
      def forward(self, x):
          x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
          x = self.drop(x)
          x = self.act(x)
          return x
  
  
  class LayerNormAct(nn.LayerNorm):
      def __init__(
              self, normalization_shape: Union[int, List[int], torch.Size], eps=1e-5, affine=True,
              apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None):
          super(LayerNormAct, self).__init__(
              normalization_shape, eps=eps, elementwise_affine=affine)
          self.drop = drop_layer() if drop_layer is not None else nn.Identity()
          act_layer = get_act_layer(act_layer)  # string -> nn.Module
          if act_layer is not None and apply_act:
              act_args = dict(inplace=True) if inplace else {}
              self.act = act_layer(**act_args)
          else:
              self.act = nn.Identity()
  
      def forward(self, x):
          x = F.layer_norm(x, self.normalized_shape,
                           self.weight, self.bias, self.eps)
          x = self.drop(x)
          x = self.act(x)
          return x
  
  
  class LayerNormAct2d(nn.LayerNorm):
      def __init__(
              self, num_channels, eps=1e-5, affine=True,
              apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None):
          super(LayerNormAct2d, self).__init__(
              num_channels, eps=eps, elementwise_affine=affine)
          self.drop = drop_layer() if drop_layer is not None else nn.Identity()
          act_layer = get_act_layer(act_layer)  # string -> nn.Module
          if act_layer is not None and apply_act:
              act_args = dict(inplace=True) if inplace else {}
              self.act = act_layer(**act_args)
          else:
              self.act = nn.Identity()
  
      def forward(self, x):
          x = F.layer_norm(
              x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
          x = self.drop(x)
          x = self.act(x)
          return x
  
apis: torch.nn.Identity torch.nn.functional.group_norm torch.nn.functional.batch_norm torch.nn.functional.layer_norm