code: def forward(ctx, x: torch.Tensor, W: torch.Tensor) -> torch.Tensor:
      # checks constraints
      assert x.shape[1] == W.shape[0], "incompatible dimensions"
      assert x.is_contiguous(), "matrix A must be contiguous"
      assert W.is_contiguous(), "matrix B must be contiguous"
      M, K = x.shape
      K, N = W.shape
      assert (
          K % 16 == 0
      ), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"
      # allocates output
      A = torch.empty((M, N // 2), device=x.device, dtype=x.dtype)
      z1 = torch.empty(
          (M, N // 2), device=x.device, dtype=x.dtype
      )  # same dimensions as the weights
      z2 = torch.empty((M, N // 2), device=x.device, dtype=x.dtype)
      # 1D launch kernel where each block gets its own program.
      grid = lambda META: (
          triton.cdiv(M, META["BLOCK_SIZE_M"])
          * triton.cdiv(N, META["BLOCK_SIZE_N"]),
      )
      gelu_partial_layer_fused_forward[grid](  # type: ignore
          x,
          W,
          A,
          z1,
          z2,
          M,
          N,
          K,
          x.stride(0),
          x.stride(1),
          W.stride(0),
          W.stride(1),
          A.stride(0),
          A.stride(1),
          # In production we would not want to hardcode these, but likely want to
          # find them via triton's autotuner.
          # BLOCK_SIZE_M=32, # type: ignore
          # BLOCK_SIZE_N=64, # type: ignore
          # BLOCK_SIZE_K=16, # type: ignore
          # GROUP_SIZE_M=8, # type: ignore
      )
      # global triton_z1
      # global triton_z2
      # triton_z1 = z1
      # triton_z2 = z2
      ctx.save_for_backward(x, W, z1, z2)
      return A
  
imports: from typing import Tuple
  import torch
  import triton
  import triton.language
  import numpy
label_desc: Use image node_id 1 for calling the PartialGeluLayer obj's underlying member method code with example usage: obj.forward(ctx, x, W) and returns: A