'has_bias': [True, False],
          'use_kernel': [True, False],
      }])
  )
  @test_util.run_in_graph_and_eager_modes
  def test_qat_matmul_model(
      self,
      shapes: Sequence[Tuple[_TensorShape, _TensorShape]],
      activation_fn: Optional[ops.Operation],
      has_bias: bool,
      use_kernel: bool,
  ):
    n = 5

// from other sources are always considered valid.
//
//go:nosplit
func validSIGPROF(mp *m, c *sigctxt) bool {
	code := int32(c.sigcode())
	setitimer := code == _SI_KERNEL
	timer_create := code == _SI_TIMER

	if !(setitimer || timer_create) {
		// The signal doesn't correspond to a profiling mechanism that the
		// runtime enables itself. There's no reason to process it, but there's

          "multiplier");
    }

    // Slicing with dynamic offsets (helper method advised)
    auto create_slice = [&](mlir::Value tensor, int depth_idx, int channel_idx,
                            bool is_kernel = false) -> mlir::Value {
      std::vector<int64_t> tensor_shape =
          mlir::cast<ShapedType>(tensor.getType()).getShape().vec();

      // Calculate offsets based on depth_idx, channel_idx and tensor_shape