def METHOD_NAME(data, kernel, strides, padding, out_dtype, output_padding):
    """Preprocess data and kernel to make the compute pattern
    of conv3d_transpose the same as conv3d"""
    batch, in_c, in_d, in_h, in_w = data.shape
    _, out_c, filter_d, filter_h, filter_w = kernel.shape
    stride_d, stride_h, stride_w = strides
    opad_d, opad_h, opad_w = output_padding
    assert opad_d < stride_d and opad_h < stride_h and opad_w < stride_w
    # dilate data
    data_dilate = dilate(data, [1, 1, stride_d, stride_h, stride_w], name="data_dilate")
    # pad data
    fpad_front, fpad_top, fpad_left, fpad_back, fpad_bottom, fpad_right = get_pad_tuple3d(
        padding, (filter_d, filter_h, filter_w)
    )
    bpad_front = filter_d - 1 - fpad_front
    bpad_back = filter_d - 1 - fpad_back + opad_d
    bpad_top = filter_h - 1 - fpad_top
    bpad_bottom = filter_h - 1 - fpad_bottom + opad_h
    bpad_left = filter_w - 1 - fpad_left
    bpad_right = filter_w - 1 - fpad_right + opad_w
    data_pad = pad(
        data_dilate,
        [0, 0, bpad_front, bpad_top, bpad_left],
        [0, 0, bpad_back, bpad_bottom, bpad_right],
        name="data_pad",
    )
    # transform kernel layout from IODHW to OIDHW, and rotate kernel by 180 degrees
    kernel_transform = te.compute(
        (out_c, in_c, filter_d, filter_h, filter_w),
        lambda o, i, d, h, w: kernel[i][o][filter_d - 1 - d][filter_h - 1 - h][filter_w - 1 - w],
        name="kernel_transform",
    )
    return data_pad, kernel_transform