code: def forward(self, q, kv, padding_mask=None, alibi_bias=None):
      B, N, C = q.shape
  
      q = (
          self.q_proj(q)
          .reshape(B, N, self.num_heads, C // self.num_heads)
          .permute(0, 2, 1, 3)
      )  # B x H x L x D
      kv = (
          self.kv_proj(kv)
          .reshape(B, -1, 2, self.num_heads, C // self.num_heads)
          .permute(2, 0, 3, 1, 4)
      )  # kv x B x H x L x D
      k, v = (
          kv[0],
          kv[1],
      )  # make torchscript happy (cannot use tensor as tuple)
  
      dtype = q.dtype
  
      if self.cosine_attention:
          # cosine attention
          attn = F.normalize(q, dim=-1) @ F.normalize(
              k, dim=-1
          ).transpose(-2, -1)
          logit_scale = torch.clamp(
              self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01))
          ).exp()
          attn = attn * logit_scale
      else:
          q = q * self.scale
          attn = q @ k.transpose(-2, -1)
  
      if alibi_bias is not None:
          attn = attn.type_as(alibi_bias)
          attn[:, : alibi_bias.size(1)] += alibi_bias
  
      if padding_mask is not None and padding_mask.any():
          attn = attn.masked_fill(
              padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),
              float("-inf"),
          )
  
      attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype)
      attn = self.attn_drop(attn)
      x = (attn @ v).transpose(1, 2)  #
      x = x.reshape(B, N, C)
      x = self.proj(x)
      x = self.proj_drop(x)
      return x
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from dataclasses import dataclass
  from fairseq.modules import LayerNorm, SamePad, SamePad2d, TransposeLast
label_desc: Use image node_id 2 for calling the EncDecAttention obj's underlying member method code with example usage: obj.forward(q, kv, padding_mask, alibi_bias) and returns: x