old_contents: import torch.nn as nn
  
  from hypergan.modules.modulated_conv2d import EqualLinear
  
  class EzNorm(nn.Module):
      def __init__(self, style_size, channels, dims, equal_linear=False, use_conv=True, dim=1):
          super(EzNorm, self).__init__()
          if equal_linear:
              self.beta = EqualLinear(style_size, channels, lr_mul=0.01)
          else:
              self.beta = nn.Linear(style_size, channels)
          if dims == 2:
              self.conv = nn.Conv1d(channels, 1, 1, 1, padding = 0)
          else:
              self.conv = nn.Conv2d(channels, 1, 1, 1, padding = 0)
          self.dim = dim
  
      def forward(self, content, style, epsilon=1e-5):
          N = content.shape[0]
          D = content.shape[self.dim]
          view = [1 for x in content.shape]
          view[0] = N
          view[self.dim] = D
  
          return content + self.beta(style).view(*view) * self.conv(content)
  
new_contents: import torch.nn as nn
  
  from hypergan.modules.modulated_conv2d import EqualLinear
  
  class EzNorm(nn.Module):
      def __init__(self, style_size, channels, dims, equal_linear=False, use_conv=True, dim=1):
          super(EzNorm, self).__init__()
          if equal_linear:
              self.beta = EqualLinear(style_size, channels, lr_mul=0.01)
          else:
              self.beta = nn.Linear(style_size, channels)
          if dims == 2:
              self.conv = nn.Conv1d(channels, 1, 1, 1, padding = 0)
          else:
              self.conv = nn.Conv2d(channels, 1, 1, 1, padding = 0)
          self.dim = dim
  
      def forward(self, content, style, epsilon=1e-5):
          N = content.shape[0]
          D = content.shape[self.dim]
          view = [1 for x in content.shape]
          view[0] = N
          view[self.dim] = D
  
          return self.beta(style).view(*view) * self.conv(content)
  
subject: move add out of ez_norm layer