(self, init_row, attr, domain_vals)
return torch.cat([f.create_tensor(init_row, attr, domain_vals) for f in self.featurizers], dim=1)
Generates the feature tensor for the list of :param`domain_vals` from
        all featurizers.

        :param init_row: (namedtuple or dict) current initial values
        :param attr: (str) attribute of row (i.e. cell) the :param values: correspond to
            and the cell to generate a feature tensor for.
        :param domain_vals: (list[str]) domain values to featurize for

        :return: Tensor with dimensions (len(values), total # of features across all featurizers)
Generates the feature tensor for the list of :param`domain_vals` from
        all featurizers.
Generates the feature tensor for the list of : param domain_vals from all featurizers .
def _gen_feat_tensor(self, init_row, attr, domain_vals):
        """
        Generates the feature tensor for the list of :param`domain_vals` from
        all featurizers.

        :param init_row: (namedtuple or dict) current initial values
        :param attr: (str) attribute of row (i.e. cell) the :param values: correspond to
            and the cell to generate a feature tensor for.
        :param domain_vals: (list[str]) domain values to featurize for

        :return: Tensor with dimensions (len(values), total # of features across all featurizers)
        """

        return torch.cat([f.create_tensor(init_row, attr, domain_vals) for f in self.featurizers], dim=1)
def _gen_feat_tensor ( self , init_row , attr , domain_vals ) : return torch . cat ( [ f . create_tensor ( init_row , attr , domain_vals ) for f in self . featurizers ] , dim = 1 )