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Complete this python program:
def _gen_overwrite_adjustment_cases(dtype):
assistant
#Here is the Python code you requested.

    """Generate test cases for overwrite adjustments.
    The algorithm used here is the same as the one used above for
    multiplicative adjustments.  The only difference is the semantics of how
    the adjustments are expected to modify the arrays.
    This is parameterized on `make_input` and `make_expected_output` functions,
    which take 2-D lists of values and transform them into desired input/output
    arrays. We do this so that we can easily test both vanilla numpy ndarrays
    and our own LabelArray class for strings.
    """
    adjustment_type = {float64_dtype: Float64Overwrite, datetime64ns_dtype: Datetime64Overwrite, int64_dtype: Int64Overwrite, bytes_dtype: ObjectOverwrite, unicode_dtype: ObjectOverwrite, object_dtype: ObjectOverwrite}[dtype]
    make_expected_dtype = as_dtype(dtype)
    missing_value = default_missing_value_for_dtype(datetime64ns_dtype)
    if (dtype == object_dtype):
        def make_overwrite_value(dtype, value):
            return str(value)
    else:
        make_overwrite_value = coerce_to_dtype
    adjustments = {}
    buffer_as_of = ([None] * 6)
    baseline = make_expected_dtype([[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]])
    buffer_as_of[0] = make_expected_dtype([[2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]])
    adjustments[1] = [adjustment_type(0, 0, 0, 0, make_overwrite_value(dtype, 1))]
    buffer_as_of[1] = make_expected_dtype([[1, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]])
    buffer_as_of[2] = buffer_as_of[1]
    adjustments[3] = [adjustment_type(1, 2, 1, 1, make_overwrite_value(dtype, 3)), adjustment_type(0, 1, 0, 0, make_overwrite_value(dtype, 4))]
    buffer_as_of[3] = make_expected_dtype([[4, 2, 2], [4, 3, 2], [2, 3, 2], [2, 2, 2], [2, 2, 2], [2, 2, 2]])
    adjustments[4] = [adjustment_type(0, 3, 2, 2, make_overwrite_value(dtype, 5))]
    buffer_as_of[4] = make_expected_dtype([[4, 2, 5], [4, 3, 5], [2, 3, 5], [2, 2, 5], [2, 2, 2], [2, 2, 2]])
    adjustments[5] = [adjustment_type(0, 4, 1, 1, make_overwrite_value(dtype, 6)), adjustment_type(2, 2, 2, 2, make_overwrite_value(dtype, 7))]
    buffer_as_of[5] = make_expected_dtype([[4, 6, 5], [4, 6, 5], [2, 6, 7], [2, 6, 5], [2, 6, 2], [2, 2, 2]])
    return _gen_expectations(baseline, missing_value, adjustments, buffer_as_of, nrows=6, perspective_offsets=(0, 1))