class_docstr: Single-head Attention layer for label-specific representations
class_docstr_tok: ["Single-head","Attention","layer","for","label-specific","representations"]
code: def forward(self, inp, batch_idxs, k_inp=None):
      residual = inp  # len_inp x d_model
      # print()
      # print(f"inp.shape: {inp.shape}")
      len_inp = inp.size(0)
      # print(f"len_inp: {len_inp}")
  
      # While still using a packed representation, project to obtain the
      # query/key/value for each head
      q_s, k_s, v_s = self.split_qkv_packed(inp, k_inp=k_inp)
      # d_l x len_inp x d_k
      # q_s is d_l x 1 x d_k
  
      # Switch to padded representation, perform attention, then switch back
      (
          q_padded,
          k_padded,
          v_padded,
          attn_mask,
          output_mask,
      ) = self.pad_and_rearrange(q_s, k_s, v_s, batch_idxs)
      # q_padded, k_padded, v_padded: (d_l * batch_size) x max_len x d_kv
      # q_s is (d_l * batch_size) x 1 x d_kv
  
      outputs_padded, attns_padded = self.attention(
          q_padded,
          k_padded,
          v_padded,
          attn_mask=attn_mask,
      )
      # outputs_padded: (d_l * batch_size) x max_len x d_kv
      # in LAL: (d_l * batch_size) x 1 x d_kv
      # on the best model, this is one value vector per label that is repeated max_len times
      if not self.q_as_matrix:
          outputs_padded = outputs_padded.repeat(
              1, output_mask.size(-1), 1
          )
      outputs = outputs_padded[output_mask]
      # outputs: (d_l * len_inp) x d_kv or LAL: (d_l * len_inp) x d_kv
      # output_mask: (d_l * batch_size) x max_len
      outputs = self.combine_v(outputs)
      # print(f"outputs shape: {outputs.shape}")
      # outputs: len_inp x d_l x d_model, whereas a normal self-attention layer gets len_inp x d_model
      if self.use_resdrop:
          if self.combine_as_self:
              outputs = self.residual_dropout(outputs, batch_idxs)
          else:
              outputs = torch.cat(
                  [
                      self.residual_dropout(
                          outputs[:, i, :], batch_idxs
                      ).unsqueeze(1)
                      for i in range(self.d_l)
                  ],
                  1,
              )
      if self.combine_as_self:
          outputs = self.layer_norm(outputs + inp)
      else:
          for l in range(self.d_l):
              outputs[:, l, :] = outputs[:, l, :] + inp
  
          outputs = self.reduce_proj(outputs)  # len_inp x d_l x d_proj
          outputs = self.layer_norm(outputs)  # len_inp x d_l x d_proj
          outputs = outputs.view(
              len_inp, -1
          ).contiguous()  # len_inp x (d_l * d_proj)
  
      return outputs, attns_padded
  
imports: import numpy
  import functools
  import sys
  import torch
  from torch.autograd import Variable
  import torch.nn
  import torch.nn.init
label_desc: Use image node_id 5 for calling the LabelAttention obj's underlying member method code with example usage: obj.forward(inp, batch_idxs, k_inp) and returns: outputs,  attns_padded