code: def _DWf_DW_dfg_kernel(
      DW,
      e,
      g,
      n_elements,
      BLOCK_SIZE: tl.constexpr,
  ):
      block_idx = tl.program_id(0)
      offsets = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
      mask = offsets < n_elements
  
      DW_row = tl.load(DW + offsets, mask=mask, other=0).to(tl.float32)
      e_row = tl.load(e + offsets, mask=mask, other=0).to(tl.float32)
      g_row = tl.load(g + offsets, mask=mask, other=0).to(tl.float32)
  
      # f = e * sigmoid(e)
      se_row = 1 / (1 + tl.exp(-e_row))
      # f = e * se
      f_row = e_row * se_row
      # h = f * g
      h_row = f_row * g_row
      # DW_f = DW * f
      DWf_row = DW_row * f_row
      # DW_dfg = DW * (se*(g - h) + h)
      DW_dfg_row = DW_row * (se_row * (g_row - h_row) + h_row)
  
      # Store derivatives in buffers
      tl.store(DW + offsets, h_row, mask=mask)
      tl.store(e + offsets, DWf_row, mask=mask)
      tl.store(g + offsets, DW_dfg_row, mask=mask)
  
file_path: unsloth/unsloth/kernels/swiglu.py
imports: import triton
  import triton.language
  import torch
  from .utils import calculate_settings
label_desc: Use image node_id 3 for calling a global function with example usage: _DWf_DW_dfg_kernel(DW, e, g, n_elements, BLOCK_SIZE) without return types