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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 apply(self, dataset, flat=False, expanded=None, ranges={}, all_values=False):
 """Evaluates the transform on the supplied dataset. Args: dataset: Dataset object to evaluate the expression on flat: Whether to flatten the returned array expanded: Whether to use the expanded expand values ranges: Dictionary for ranges for normalization all_values: Whether to evaluate on all values Whether to evaluate on all available values, for some element types, such as Graphs, this may include values not included in the referenced column Returns: values: NumPy array computed by evaluating the expression """

        dimension = self.dimension
        if expanded is None:
            expanded = not ((dataset.interface.gridded and dimension in dataset.kdims) or
                            (dataset.interface.multi and dataset.interface.isscalar(dataset, dimension)))
        if isinstance(dataset, Graph):
            if dimension in dataset.kdims and all_values:
                dimension = dataset.nodes.kdims[2]
            dataset = dataset if dimension in dataset else dataset.nodes
        data = dataset.dimension_values(dimension, expanded=expanded, flat=flat)
        for o in self.ops:
            args = o['args']
            fn_args = [data]
            for arg in args:
                if isinstance(arg, dim):
                    arg = arg.apply(dataset, flat, expanded, ranges, all_values)
                fn_args.append(arg)
            args = tuple(fn_args[::-1] if o['reverse'] else fn_args)
            eldim = dataset.get_dimension(dimension)
            drange = ranges.get(eldim.name, {})
            drange = drange.get('combined', drange)
            kwargs = o['kwargs']
            if o['fn'] is norm and drange != {} and not ('min' in kwargs and 'max' in kwargs):
                data = o['fn'](data, *drange)
            else:
                data = o['fn'](*args, **kwargs)
        return data