def METHOD_NAME(
    ifm_layout, ofm_layout, ifm_channels, expected_cycles
):
    ofm_channels = 10
    ifm_height = 123
    ifm_width = 155
    ifm_shape = (
        (1, ifm_height, ifm_width, ifm_channels)
        if ifm_layout == "NHWC"
        else (1, ifm_height, 1 + ((ifm_channels - 1) // 16), ifm_width, 16)
    )
    weight_shape = (ofm_channels, 3, 3, ifm_channels)
    scale_bias_shape = (ofm_channels, 10)
    ifm = te.placeholder(ifm_shape, dtype="int8")
    weight = te.placeholder(weight_shape, dtype="int8")
    scale_bias = te.placeholder(scale_bias_shape, dtype="uint8")
    lut = te.placeholder((), dtype="uint8")
    out = conv2d_compute(
        ifm=ifm,
        weight=weight,
        scale_bias=scale_bias,
        lut=lut,
        ifm_scale=1,
        ifm_zero_point=0,
        ofm_scale=1,
        ofm_zero_point=0,
        weight_zero_point=0,
        strides=(1, 1),
        padding=(0, 0, 0, 0),
        dilation=(1, 1),
        activation="NONE",
        clip_min=0,
        clip_max=0,
        upscale="NONE",
        rounding_mode="TFL",
        ifm_layout=ifm_layout,
        ofm_layout=ofm_layout,
    )
    device_config = cs.EthosuDeviceConfig("ethos-u55-256")
    part = match_ethosu_conv2d(out, device_config)
    ofm_shape = [int(i) for i in part.subgraph.output_tensor.shape]
    # Add inputs and outputs to the part
    input_tensor = cs.Tensor(ifm_shape, "int8")
    part.set_input(0, input_tensor)
    weight_tensor = cs.Tensor(weight_shape, "int8")
    part.set_input(1, weight_tensor)
    scale_bias_tensor = cs.Tensor(scale_bias_shape, "int8")
    part.set_input(2, scale_bias_tensor)
    output_tensor = cs.Tensor(ofm_shape, "int8")
    part.set_output(output_tensor)
    # Create a stripe of a size of the output tensor
    order = [1, 2, 3, 4] if ofm_layout == "NHWC" else [1, 2, 4, 3, 0]
    stripes = [1] * len(order)
    offset = [0] * len(order)
    stripe_config = cs.StripeConfig(ofm_shape, ofm_shape, ofm_shape, order, stripes, offset)
    block = part.get_block_config(stripe_config)
    # Since we dont know the values of the variables we passed to the get_valid_block_configs in
    # the matcher, best we can do is to verify the compute cycle count since the channels have a
    # significant effect on it
    assert block.compute_cycles == expected_cycles