'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

          "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