code: def forward(
      ctx,
      use_time_mask,
      is_training,
      heads,
      inputs,
      input_weights,
      output_weights,
      input_biases,
      output_biases,
      pad_mask,
      mask_additive,
      dropout_prob,
  ):
      use_biases_t = torch.tensor([input_biases is not None])
      heads_t = torch.tensor([heads])
      dropout_prob_t = torch.tensor([dropout_prob])
      null_tensor = torch.tensor([])
      use_mask = pad_mask is not None
      mask_additive_t = torch.tensor([mask_additive])
  
      if use_biases_t[0]:
          if not mask_additive:
              (
                  input_lin_results,
                  softmax_results,
                  dropout_results,
                  dropout_mask,
                  matmul2_results,
                  outputs,
              ) = fast_multihead_attn.self_attn_bias_forward(
                  use_mask,
                  use_time_mask,
                  is_training,
                  heads,
                  inputs,
                  input_weights,
                  output_weights,
                  input_biases,
                  output_biases,
                  pad_mask if use_mask else null_tensor,
                  dropout_prob,
              )
              # fast_self_multihead_attn_bias.forward()                           \
              ctx.save_for_backward(
                  use_biases_t,
                  heads_t,
                  matmul2_results,
                  dropout_results,
                  softmax_results,
                  null_tensor,
                  null_tensor,
                  mask_additive_t,
                  input_lin_results,
                  inputs,
                  input_weights,
                  output_weights,
                  dropout_mask,
                  dropout_prob_t,
              )
  
          else:
              (
                  input_lin_results,
                  bmm1_results,
                  dropout_results,
                  dropout_mask,
                  matmul2_results,
                  outputs,
              ) = fast_multihead_attn.self_attn_bias_additive_mask_forward(
                  use_mask,
                  use_time_mask,
                  is_training,
                  heads,
                  inputs,
                  input_weights,
                  output_weights,
                  input_biases,
                  output_biases,
                  pad_mask if use_mask else null_tensor,
                  dropout_prob,
              )
              # fast_self_multihead_attn_bias_additive_mask.forward(                           \
              ctx.save_for_backward(
                  use_biases_t,
                  heads_t,
                  matmul2_results,
                  dropout_results,
                  null_tensor,
                  bmm1_results,
                  pad_mask,
                  mask_additive_t,
                  input_lin_results,
                  inputs,
                  input_weights,
                  output_weights,
                  dropout_mask,
                  dropout_prob_t,
              )
  
      else:
          (
              input_lin_results,
              softmax_results,
              dropout_results,
              dropout_mask,
              matmul2_results,
              outputs,
          ) = fast_multihead_attn.self_attn_forward(
              use_mask,
              use_time_mask,
              is_training,
              heads,
              inputs,
              input_weights,
              output_weights,
              pad_mask if use_mask else null_tensor,
              dropout_prob,
          )
          # fast_self_multihead_attn.forward(                           \
          ctx.save_for_backward(
              use_biases_t,
              heads_t,
              matmul2_results,
              dropout_results,
              softmax_results,
              null_tensor,
              null_tensor,
              mask_additive_t,
              input_lin_results,
              inputs,
              input_weights,
              output_weights,
              dropout_mask,
              dropout_prob_t,
          )
      return outputs.detach()
  
imports: import torch
  import fast_multihead_attn
label_desc: Use image node_id 1 for calling the FastSelfAttnFunc obj's underlying member method code with example usage: obj.forward(ctx, use_time_mask, is_training, heads, inputs, input_weights, output_weights, input_biases, output_biases, pad_mask, mask_additive, dropout_prob) and returns: outputs