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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 transpose(self, *args, **kwargs):
 """ Transpose index and columns. Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property :attr:`.T` is an accessor to the method :meth:`transpose`. Parameters copy : bool, default False If True, the underlying data is copied. Otherwise (default), no copy is made if possible. *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with numpy. Returns ------- DataFrame The transposed DataFrame. See Also -------- numpy.transpose : Permute the dimensions of a given array. Notes ----- Transposing a DataFrame with mixed dtypes will result in a homogeneous DataFrame with the `object` dtype. In such a case, a copy of the data is always made. Examples -------- **Square DataFrame with homogeneous dtype** col1 col2 0 1 3 1 2 4 0 1 col1 1 2 col2 3 4 When the dtype is homogeneous in the original DataFrame, we get a transposed DataFrame with the same dtype: col1 int64 col2 int64 dtype: object 0 int64 1 int64 dtype: object **Non-square DataFrame with mixed dtypes** name score employed kids 0 Alice 9.5 False 0 1 Bob 8.0 True 0 0 1 name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the `object` dtype: name object score float64 employed bool kids int64 dtype: object 0 object 1 object dtype: object """

        nv.validate_transpose(args, dict())
        return super().transpose(1, 0, **kwargs)