def METHOD_NAME(self, model_type, cost_metric):
    """
    test the conv2d split after and before split_module call with stride
    """
    model = get_model(model_type)
    orig_conv_op = _get_layers(model, model_type)[0]
    org_conv_op_shape = orig_conv_op.output_shape
    layer1 = Layer(orig_conv_op, orig_conv_op.name, output_shape=org_conv_op_shape)
    rank = cost_calculator.WeightSvdCostCalculator.calculate_rank_given_comp_ratio(layer1, 0.5, cost_metric)
    svd_lib_ref = pymo.GetSVDInstance()
    pymo_utils.PymoSvdUtils.configure_layers_in_pymo_svd([layer1], cost_metric, svd_lib_ref, pymo.TYPE_SINGLE)
    split_conv_op1, split_conv_op2 = WeightSvdModuleSplitter.split_module(model, layer1.module, rank, svd_lib_ref)
    split_conv_output = split_conv_op2.output_shape
    assert org_conv_op_shape == split_conv_output
    # check the bias value after split.
    assert len(split_conv_op2.get_weights()) == len(orig_conv_op.get_weights())
    if len(orig_conv_op.get_weights()) > 1:
        orig_bias_out = orig_conv_op.get_weights()[1]
        split_bias_out = split_conv_op2.get_weights()[1]
        assert np.allclose(orig_bias_out, split_bias_out, atol=1e-4)
    # First split conv op should also have bias
    assert len(split_conv_op1.get_weights()) == 2
    # Length of the bias of first conv split should be equal to rank
    assert len(split_conv_op1.get_weights()[1]) == rank