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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def check_compression_gathering(self, ds):
 """ At the current time the netCDF interface does not provide for packing data. However a simple packing may be achieved through the use of the optional NUG defined attributes scale_factor and add_offset . After the data values of a variable have been read, they are to be multiplied by the scale_factor , and have add_offset added to them. If both attributes are present, the data are scaled before the offset is added. When scaled data are written, the application should first subtract the offset and then divide by the scale factor. The units of a variable should be representative of the unpacked data. This standard is more restrictive than the NUG with respect to the use of the scale_factor and add_offset attributes; ambiguities and precision problems related to data type conversions are resolved by these restrictions. If the scale_factor and add_offset attributes are of the same data type as the associated variable, the unpacked data is assumed to be of the same data type as the packed data. However, if the scale_factor and add_offset attributes are of a different data type from the variable (containing the packed data) then the unpacked data should match the type of these attributes, which must both be of type float or both be of type double . An additional restriction in this case is that the variable containing the packed data must be of type byte , short or int . It is not advised to unpack an int into a float as there is a potential precision loss. When data to be packed contains missing values the attributes that indicate missing values ( _FillValue , valid_min , valid_max , valid_range ) must be of the same data type as the packed data. See Section 2.5.1, “Missing Data” for a discussion of how applications should treat variables that have attributes indicating both missing values and transformations defined by a scale and/or offset. :param netCDF4.Dataset ds: An open netCDF dataset :rtype: list :return: List of results """

        ret_val = []
        for compress_var in ds.get_variables_by_attributes(compress=lambda s: s is not None):
            valid = True
            reasoning = []
            # puts the referenced variable being compressed into a set
            compress_set = set(compress_var.compress.split(' '))
            if compress_var.ndim != 1:
                valid = False
                reasoning.append("Compression variable {} may only have one dimension".format(compress_var.name))
            # ensure compression variable is a proper index, and thus is an
            # signed or unsigned integer type of some sort
            if compress_var.dtype.kind not in {'i', 'u'}:
                valid = False
                reasoning.append("Compression variable {} must be an integer type to form a proper array index".format(compress_var.name))
            # make sure all the variables referred to are contained by the
            # variables.
            if not compress_set.issubset(ds.dimensions):
                not_in_dims = sorted(compress_set.difference(ds.dimensions))
                valid = False
                reasoning.append("The following dimensions referenced by the compress attribute of variable {} do not exist: {}".format(compress_var.name, not_in_dims))

            result = Result(BaseCheck.MEDIUM,
                            valid,
                            self.section_titles['8.2'],
                            reasoning)
            ret_val.append(result)

        return ret_val