def METHOD_NAME(
    src,
    weights,
    stride,
    padding,
    dilation,
    groups,
    channel_last=False,
    out_dtype="float32",
    **kwargs,
):
    """Convolution operator in NCHW layout.
    Parameters
    ----------
    src : tvm.te.Tensor
        4-D with shape [batch, in_channel, in_height, in_width]
    weights : tvm.te.Tensor
        4-D with shape [num_filter, in_channel, filter_height, filter_width]
    stride : int or a list/tuple of two ints
        Stride size, or [stride_height, stride_width]
    padding : int or a list/tuple of 2 or 4 ints
        padding size, or
        [pad_height, pad_width] for 2 ints, or
        [pad_top, pad_left, pad_bottom, pad_right] for 4 ints
    dilation: int or a list/tuple of two ints
        dilation size, or [dilation_height, dilation_width]
    groups: str
        input data layout: NCHW or NHWC
    channel_last: bool
        chose if input/output data format is in channel_last format(NHWC) or
        in plain format(NCHW)
    out_dtype: str
        output datatype: now only support float32
    Returns
    -------
    Output : tvm.te.Tensor
        4-D with shape [batch, out_channel, out_height, out_width]
    """
    assert isinstance(stride, int) or len(stride) == 2
    assert isinstance(dilation, int) or len(dilation) == 2
    if isinstance(stride, int):
        stride_h = stride_w = stride
    else:
        stride_h, stride_w = stride
    if isinstance(dilation, int):
        dilation_h = dilation_w = dilation
    else:
        dilation_h, dilation_w = dilation
    pre_cast = src.dtype == "float32"
    post_cast = out_dtype == "float32"
    if channel_last:
        batch, in_height, in_width, _ = src.shape
        kernel_h, kernel_w, _, num_filter = weights.shape
    else:
        batch, _, in_height, in_width = src.shape
        num_filter, _, kernel_h, kernel_w = weights.shape
    dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
    dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
        padding, (dilated_kernel_h, dilated_kernel_w)
    )
    out_channel = num_filter
    out_height = (in_height - dilated_kernel_h + pad_top + pad_down) // stride_h + 1
    out_width = (in_width - dilated_kernel_w + pad_left + pad_right) // stride_w + 1
    if channel_last:
        out_shape = (batch, out_height, out_width, out_channel)
    else:
        out_shape = (batch, out_channel, out_height, out_width)
    return te.extern(
        out_shape,
        [src, weights],
        lambda ins, outs: tvm.tir.call_packed(
            "tvm.contrib.dnnl.conv2d",
            ins[0],
            ins[1],
            outs[0],
            pad_top,
            pad_down,
            pad_left,
            pad_right,
            stride[0],
            stride[1],
            groups,
            channel_last,
            pre_cast,
            post_cast,
        ),
        name="C",
        dtype=out_dtype,
        **kwargs,
    )