def METHOD_NAME(op: relay.Call, inputs: List[te.Tensor]) -> te.Tensor:
    """Lower a fused conv2d+bias+activation using cuDNN."""
    conv_dtype = op.checked_type.dtype
    if op.op.name == "nn.relu":
        activation_mode = 1  # Relu
        conv2d = op.args[0].args[0]
    else:
        activation_mode = 5  # Identity
        conv2d = op.args[0]
    conv_mode = 1
    tensor_format = 0
    algo = 1
    pad = conv2d.attrs["padding"]
    strides = conv2d.attrs["strides"]
    dilation = conv2d.attrs["dilation"]
    groups = conv2d.attrs["groups"]
    oshape = cudnn.conv_output_shape(
        tensor_format,
        pad,
        strides,
        dilation,
        inputs[0].shape,
        inputs[1].shape,
        inputs[0].dtype,
        conv_dtype,
        groups,
    )
    return te.extern(
        oshape,
        inputs,
        lambda ins, outs: tvm.tir.call_packed(
            "tvm.contrib.cudnn.conv2d+bias+act.forward",
            conv_mode,
            tensor_format,
            algo,
            pad[0],
            pad[1],
            strides[0],
            strides[1],
            dilation[0],
            dilation[1],
            activation_mode,
            0,
            ins[0],
            ins[1],
            ins[2],
            outs[0],
            conv_dtype,
            groups,
        ),
        name="y",
    )