code: def from_dict(cls, data, intersect=False, orient='items', dtype=None):
          
          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)
docs: 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