def METHOD_NAME(
    batch,
    in_channel,
    in_size,
    num_filter,
    kernel,
    stride,
    padding,
    dilation=1,
    add_bias=False,
    add_relu=False,
    devices="cuda",
):
    """Test the conv3d with tensorcore for ndhwc layout"""
    pad_front, pad_top, pad_left, pad_back, pad_bottom, pad_right = get_pad_tuple3d(
        padding, (kernel, kernel, kernel)
    )
    padding_sum = pad_front + pad_top + pad_left + pad_back + pad_bottom + pad_right
    print(
        "Workload: (%d, %d, %d, %d, %d, %d, %d, %d)"
        % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)
    )
    in_depth = in_height = in_width = in_size
    dtype = "float16"
    A = te.placeholder((batch, in_depth, in_height, in_width, in_channel), dtype, name="A")
    W = te.placeholder((kernel, kernel, kernel, in_channel, num_filter), dtype, name="W")
    bias = te.placeholder((1, 1, 1, 1, num_filter), dtype, name="bias")
    a_shape = get_const_tuple(A.shape)
    w_shape = get_const_tuple(W.shape)
    bias_shape = get_const_tuple(bias.shape)
    @memoize("topi.tests.test_topi_conv3d_ndhwc.verify_conv3d_ndhwc")
    def get_ref_data():
        a_np = np.random.uniform(size=a_shape).astype(dtype)
        w_np = np.random.uniform(size=w_shape).astype(dtype)
        b_np = np.random.uniform(size=bias_shape).astype(dtype)
        dw_np = tvm.topi.testing.dilate_python(w_np, (1, 1, 1, dilation, dilation))
        c_np = tvm.topi.testing.conv3d_ndhwc_python(a_np, dw_np, stride, padding)
        if add_bias:
            b_np = np.random.uniform(size=bias_shape).astype(dtype)
            c_np += b_np
        if add_relu:
            c_np = np.maximum(c_np, 0)
        return a_np, w_np, b_np, c_np
    a_np, w_np, b_np, c_np = get_ref_data()
    def check_device(device):
        dev = tvm.device(device, 0)
        print("Running on target: %s" % device)
        with tvm.target.Target(device):
            fcompute, fschedule = tvm.topi.testing.dispatch(
                device, _conv3d_ndhwc_tensorcore_implement
            )
            C = fcompute(A, W, stride, padding, dilation, 1, "float16")
            if add_bias:
                C = topi.add(C, bias)
            if add_relu:
                C = topi.nn.relu(C)
            s = fschedule([C])
        a = tvm.nd.array(a_np, dev)
        w = tvm.nd.array(w_np, dev)
        b = tvm.nd.array(b_np, dev)
        c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), dev)
        if add_bias:
            func = tvm.build(
                s,
                [A, W, bias, C],
                device,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
                % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
            )
            func(a, w, b, c)
        else:
            func = tvm.build(
                s,
                [A, W, C],
                device,
                name="relu_%d_%d_%d_%d_%d_%d_%d_%d"
                % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation),
            )
            func(a, w, c)
        # Tensorcores are very inaccurate, with large shapes, the accumulation
        # error is high especially away from 1. We disable atol as it is very
        # large for these numbers that are far away from 1.
        tvm.testing.assert_allclose(c.numpy(), c_np, atol=1e200, rtol=0.01)
    check_device(devices)