code: def forward(ctx, x, normalized_shape, weight, bias, eps):
      # allocate output
      y = torch.empty_like(x)
      # reshape input data into 2D tensor
      x_arg = x.reshape(-1, x.shape[-1])
      M, N = x_arg.shape
      mean = torch.empty((M,), dtype=torch.float32, device="cuda")
      rstd = torch.empty((M,), dtype=torch.float32, device="cuda")
      # Less than 64KB per feature: enqueue fused kernel
      MAX_FUSED_SIZE = 65536 // x.element_size()
      BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
      if N > BLOCK_SIZE:
          raise RuntimeError(
              "This layer norm doesn't support feature dim >= 64KB."
          )
      # heuristics for number of warps
      num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
      # enqueue kernel
      _layer_norm_fwd_fused[(M,)](  #
          x_arg,
          y,
          weight,
          bias,
          mean,
          rstd,  #
          x_arg.stride(0),
          N,
          eps,  #
          BLOCK_SIZE=BLOCK_SIZE,
          num_warps=num_warps,
          num_ctas=1,
      )
      ctx.save_for_backward(x, weight, bias, mean, rstd)
      ctx.BLOCK_SIZE = BLOCK_SIZE
      ctx.num_warps = num_warps
      ctx.eps = eps
      return y
  
file_path: triton/python/tutorials/05-layer-norm.py
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 1 for calling the LayerNorm obj's underlying member method code with example usage: obj.forward(ctx, x, normalized_shape, weight, bias, eps) and returns: y