def METHOD_NAME(
    data, transformed_kernel, bias, padding, stride, nthreads=1, algorithm=ConvolutionAlgorithm.AUTO
):
    """Create an extern op to do inference convolution of 4D tensor data and
    4D pre-transformed tensor kernel and 1D tensor bias with nnpack.
    Parameters
    ----------
    data : Tensor
        data 4D tensor input[batch][input_channels][input_height][input_width] of
        FP32 elements.
    transformed_kernel : Tensor
        transformed_kernel 4D tensor kernel[output_channels][input_channels][tile]
        [tile] of FP32 elements.
    bias : Tensor
        bias 1D array bias[output_channels][input_channels][kernel_height]
        [kernel_width] of FP32 elements.
    padding : list
        padding A 4-dim list of [pad_top, pad_bottom, pad_left, pad_right],
        which indicates the padding around the feature map.
    stride : list
        stride A 2-dim list of [stride_height, stride_width], which indicates
        the stride.
    Returns
    -------
    output : Tensor
        output 4D tensor output[batch][output_channels][output_height][output_width]
        of FP32 elements.
    """
    assert algorithm in (ConvolutionAlgorithm.WT_8x8, ConvolutionAlgorithm.WT_8x8_FP16)
    assert isinstance(padding, list) and len(padding) == 4
    assert isinstance(stride, list) and len(stride) == 2
    batch, _, input_height, input_width = data.shape
    output_channels, _, _, _ = transformed_kernel.shape
    kernel_height, kernel_width = (3, 3)
    idxdiv = te.indexdiv
    output_height = idxdiv(input_height + padding[0] + padding[1] - kernel_height, stride[0]) + 1
    output_width = idxdiv(input_width + padding[0] + padding[1] - kernel_width, stride[1]) + 1
    return te.extern(
        (batch, output_channels, output_height, output_width),
        [data, transformed_kernel, bias] if bias is not None else [data, transformed_kernel],
        lambda ins, outs: tvm.tir.call_packed(
            "tvm.contrib.nnpack.convolution_inference_without_weight_transform",
            ins[0],
            ins[1],
            ins[2] if bias is not None else 0,
            outs[0],
            padding[0],
            padding[1],
            padding[2],
            padding[3],
            stride[0],
            stride[1],
            nthreads,
            algorithm,
        ),
        name="C",
        dtype="float32",
    )