code: def forward(
      self, x: torch.Tensor, y: torch.Tensor, mask: bool = False
  ):
      queries = self.queries_proj_layer(x)
      keys = self.keys_proj_layer(y)
      values = self.values_proj_layer(y)
      attention = F.softmax(
          torch.matmul(queries, keys.transpose(-2, -1))
          * self.proj_dim_isqrt,
          dim=-1,
      )
      if mask:
          attention = torch.triu(attention, diagonal=1)
      output = torch.matmul(attention, values)
      return output
  
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 AttentionHead obj's underlying member method code with example usage: obj.forward(x, y, mask) and returns: output