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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 to_pandas(self, is_transposed=False):
 """Convert this object into a Pandas DataFrame from the partitions. Args: is_transposed: A flag for telling this object that the external representation is transposed, but not the internal. Returns: A Pandas DataFrame """

        # In the case this is transposed, it is easier to just temporarily
        # transpose back then transpose after the conversion. The performance
        # is the same as if we individually transposed the blocks and
        # concatenated them, but the code is much smaller.
        if is_transposed:
            return self.transpose().to_pandas(False).T
        else:
            retrieved_objects = [
                [obj.to_pandas() for obj in part] for part in self.partitions
            ]
            if all(
                isinstance(part, pandas.Series)
                for row in retrieved_objects
                for part in row
            ):
                axis = 0
            elif all(
                isinstance(part, pandas.DataFrame)
                for row in retrieved_objects
                for part in row
            ):
                axis = 1
            else:
                ErrorMessage.catch_bugs_and_request_email(True)
            df_rows = [
                pandas.concat([part for part in row], axis=axis)
                for row in retrieved_objects
                if not all(part.empty for part in row)
            ]
            if len(df_rows) == 0:
                return pandas.DataFrame()
            else:
                return pandas.concat(df_rows)