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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 from_dict(cls, data, intersect=False, orient='items', dtype=None):
 """ Construct Panel from dict of DataFrame objects. Parameters data : dict {field : DataFrame} intersect : boolean Intersect indexes of input DataFrames orient : {'items', 'minor'}, default 'items' The "orientation" of the data. If the keys of the passed dict should be the items of the result panel, pass 'items' (default). Otherwise if the columns of the values of the passed DataFrame objects should be the items (which in the case of mixed-dtype data you should do), instead pass 'minor' dtype : dtype, default None Data type to force, otherwise infer Returns ------- Panel """

        from collections import defaultdict

        orient = orient.lower()
        if orient == 'minor':
            new_data = defaultdict(OrderedDict)
            for col, df in data.items():
                for item, s in df.items():
                    new_data[item][col] = s
            data = new_data
        elif orient != 'items':  # pragma: no cover
            raise ValueError('Orientation must be one of {items, minor}.')

        d = cls._homogenize_dict(cls, data, intersect=intersect, dtype=dtype)
        ks = list(d['data'].keys())
        if not isinstance(d['data'], OrderedDict):
            ks = list(sorted(ks))
        d[cls._info_axis_name] = Index(ks)
        return cls(**d)