code: def forward(
      ctx,
      use_time_mask,
      is_training,
      heads,
      inputs,
      lyr_nrm_gamma_weights,
      lyr_nrm_beta_weights,
      input_weights,
      output_weights,
      pad_mask,
      dropout_prob,
  ):
      heads_t = torch.tensor([heads])
      dropout_prob_t = torch.tensor([dropout_prob])
      null_tensor = torch.tensor([])
      use_mask = pad_mask is not None
  
      (
          lyr_nrm_results,
          lyr_nrm_mean,
          lyr_nrm_invvar,
          input_lin_results,
          softmax_results,
          dropout_results,
          dropout_mask,
          matmul2_results,
          dropout_add_mask,
          outputs,
      ) = fast_multihead_attn.self_attn_norm_add_forward(
          use_mask,
          use_time_mask,
          is_training,
          heads,
          inputs,
          lyr_nrm_gamma_weights,
          lyr_nrm_beta_weights,
          input_weights,
          output_weights,
          pad_mask if use_mask else null_tensor,
          dropout_prob,
      )
      # fast_self_multihead_attn_norm_add.forward(                 \
  
      ctx.save_for_backward(
          heads_t,
          matmul2_results,
          dropout_results,
          softmax_results,
          input_lin_results,
          lyr_nrm_results,
          lyr_nrm_mean,
          lyr_nrm_invvar,
          inputs,
          lyr_nrm_gamma_weights,
          lyr_nrm_beta_weights,
          input_weights,
          output_weights,
          dropout_mask,
          dropout_add_mask,
          dropout_prob_t,
      )
  
      return outputs.detach()
  
imports: import torch
  import fast_multihead_attn
label_desc: Use image node_id 1 for calling the FastSelfAttnNormAddFunc obj's underlying member method code with example usage: obj.forward(ctx, use_time_mask, is_training, heads, inputs, lyr_nrm_gamma_weights, lyr_nrm_beta_weights, input_weights, output_weights, pad_mask, dropout_prob) and returns: outputs