%res_p3 = "tfl.cumsum"(%arg, %axis_p3) {exclusive = false, reverse = false} : (tensor<1x2x1x3xf32>, tensor<i32>) -> tensor<1x2x1x3xf32>
  func.return %res_m4, %res_m3, %res_m2, %res_m1, %res_00, %res_p1, %res_p2, %res_p3 : tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1x3xf32>

    dilations = [1, 1, 1, 1],
    explicit_paddings = [],
    padding = "VALID",
    strides = [1, 1, 1, 1],
    use_cudnn_on_gpu = true
  } : (tensor<1x2x2x2xf32>, tensor<4xi32>, tensor<1x1x1x2xf32>) -> tensor<2x2x1x2xf32>
  func.return %result : tensor<2x2x1x2xf32>
}


// CHECK-LABEL: @conv3d_backprop_filter