code: def forward(
      self, x: torch.nn.Module, y: torch.nn.Module, mask: bool = False
  ):
      # x.size = (Nx, c1), y.size = (Ny, c2)
      x_norm = self.norm_layer_x(x)
      y_norm = self.norm_layer_y(y)
      temp = torch.cat(
          [layer(x_norm, y_norm, mask) for layer in self.module_list],
          dim=-1,
      )
      x = x + self.linear(temp)
      return self.dense(x)
  
file_path: nebuly/optimization/open_alpha_tensor/open_alpha_tensor/core/modules/attention.py
imports: import torch
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the AlphaMultiHeadAttention obj's underlying member method code with example usage: obj.forward(x, y, mask) and returns: self