code: def _dropout_add_layer_norm_subset_backward(
      dz,
      dx,
      x,
      x0,
      dmask,
      mu,
      rsigma,
      gamma,
      colscale,
      x0_subset,
      out_subset,
      dropout_p,
      rowscale_const,
      x0_numrows,
      has_residual,
      is_rms_norm=False,
  ):
      """Assume that arguments are contiguous and aligned to 16 bytes
      dx == None means that it was a post-norm architecture
      (x = drop(x0) + residual was not returned in the fwd).
      x0 must not be None if we have colscale.
      """
      hidden_size = gamma.numel()
      xmat = x.view((-1, hidden_size))
      dzmat = dz.view(-1, hidden_size)
      dxmat = dx.view(xmat.shape) if dx is not None else None
      x0mat = x0.view((-1, hidden_size)) if x0 is not None else None
      x0_subset = x0_subset.view(-1) if x0_subset is not None else None
      out_subset = (
          out_subset.view(-1) if out_subset is not None else None
      )
      if colscale is not None:
          assert (
              x0 is not None
          ), "x0 is required to compute the gradient of colscale"
      (
          dx0mat,
          dresidualmat,
          dgamma,
          dbeta,
          _,
          _,
          *rest,
      ) = dropout_layer_norm.dropout_add_ln_bwd(
          dzmat,
          dxmat,
          xmat,
          x0mat,
          dmask,
          mu,
          rsigma,
          gamma,
          None,
          colscale,
          x0_subset,
          out_subset,
          dropout_p,
          rowscale_const,
          x0_numrows,
          has_residual,
          is_rms_norm,
      )
      # dresidualmat is None if not has_residual
      if colscale is None:
          return dx0mat, dresidualmat, dgamma, dbeta
      else:
          dcolscale = rest[0]
          return dx0mat, dresidualmat, dgamma, dbeta, dcolscale
  
imports: import dropout_layer_norm
  import torch
  from torch.nn import init
label_desc: Use image node_id 5 for calling a global function with example usage: _dropout_add_layer_norm_subset_backward(dz, dx, x, x0, dmask, mu, rsigma, gamma, colscale, x0_subset, out_subset, dropout_p, rowscale_const, x0_numrows, has_residual, is_rms_norm) and returns: dx0mat,  dresidualmat,  dgamma,  dbeta, dx0mat,  dresidualmat,  dgamma,  dbeta,  dcolscale