# Buggy code
def astype_nansafe(
    arr: np.ndarray, dtype: DtypeObj, copy: bool = True, skipna: bool = False
) -> ArrayLike:
    """
    Cast the elements of an array to a given dtype a nan-safe manner.

    Parameters
    ----------
    arr : ndarray
    dtype : np.dtype or ExtensionDtype
    copy : bool, default True
        If False, a view will be attempted but may fail, if
        e.g. the item sizes don't align.
    skipna: bool, default False
        Whether or not we should skip NaN when casting as a string-type.

    Raises
    ------
    ValueError
        The dtype was a datetime64/timedelta64 dtype, but it had no unit.
    """
    if arr.ndim > 1:
        # Make sure we are doing non-copy ravel and reshape.
        flags = arr.flags
        flat = arr.ravel("K")
        result = astype_nansafe(flat, dtype, copy=copy, skipna=skipna)
        order = "F" if flags.f_contiguous else "C"
        return result.reshape(arr.shape, order=order)

    # We get here with 0-dim from sparse
    arr = np.atleast_1d(arr)

    # dispatch on extension dtype if needed
    if isinstance(dtype, ExtensionDtype):
        return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)

    elif not isinstance(dtype, np.dtype):
        raise ValueError("dtype must be np.dtype or ExtensionDtype")

    if arr.dtype.kind in ["m", "M"] and (
        issubclass(dtype.type, str) or dtype == object
    ):
        from pandas.core.construction import ensure_wrapped_if_datetimelike

        arr = ensure_wrapped_if_datetimelike(arr)
        return arr.astype(dtype, copy=copy)

    if issubclass(dtype.type, str):
        return lib.ensure_string_array(arr, skipna=skipna, convert_na_value=False)

    elif is_datetime64_dtype(arr):
        if dtype == np.int64:
            if isna(arr).any():
                raise ValueError("Cannot convert NaT values to integer")
            return arr.view(dtype)

        # allow frequency conversions
        if dtype.kind == "M":
            return arr.astype(dtype)

        raise TypeError(f"cannot astype a datetimelike from [{arr.dtype}] to [{dtype}]")

    elif is_timedelta64_dtype(arr):
        if dtype == np.int64:
            if isna(arr).any():
                raise ValueError("Cannot convert NaT values to integer")
            return arr.view(dtype)

        elif dtype.kind == "m":
            return astype_td64_unit_conversion(arr, dtype, copy=copy)

        raise TypeError(f"cannot astype a timedelta from [{arr.dtype}] to [{dtype}]")

    elif np.issubdtype(arr.dtype, np.floating) and np.issubdtype(dtype, np.integer):
        if not np.isfinite(arr).all():
            raise ValueError("Cannot convert non-finite values (NA or inf) to integer")

    elif is_object_dtype(arr):
        # work around NumPy brokenness, #1987
        if np.issubdtype(dtype.type, np.integer):
            return lib.astype_intsafe(arr, dtype)

        # if we have a datetime/timedelta array of objects
        # then coerce to a proper dtype and recall astype_nansafe

        elif is_datetime64_dtype(dtype):
            from pandas import to_datetime

            return astype_nansafe(to_datetime(arr).values, dtype, copy=copy)
        elif is_timedelta64_dtype(dtype):
            from pandas import to_timedelta

            return astype_nansafe(to_timedelta(arr)._values, dtype, copy=copy)

    if dtype.name in ("datetime64", "timedelta64"):
        msg = (
            f"The '{dtype.name}' dtype has no unit. Please pass in "
            f"'{dtype.name}[ns]' instead."
        )
        raise ValueError(msg)

    if copy or is_object_dtype(arr) or is_object_dtype(dtype):
        # Explicit copy, or required since NumPy can't view from / to object.
        return arr.astype(dtype, copy=True)

    return arr.view(dtype)


# Traceback (TypeError)
In [10]: idx._data.astype('int32').astype("int32", casting="safe")
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-10-2c2a4a677a5c> in <module>
----> 1 idx._data.astype('int32').astype("int32", casting="safe")

TypeError: Cannot cast array from dtype('int64') to dtype('int32') according to the rule 'safe'

# Fixed code
def astype_nansafe(
    arr: np.ndarray, dtype: DtypeObj, copy: bool = True, skipna: bool = False
) -> ArrayLike:
    """
    Cast the elements of an array to a given dtype a nan-safe manner.

    Parameters
    ----------
    arr : ndarray
    dtype : np.dtype or ExtensionDtype
    copy : bool, default True
        If False, a view will be attempted but may fail, if
        e.g. the item sizes don't align.
    skipna: bool, default False
        Whether or not we should skip NaN when casting as a string-type.

    Raises
    ------
    ValueError
        The dtype was a datetime64/timedelta64 dtype, but it had no unit.
    """
    if arr.ndim > 1:
        # Make sure we are doing non-copy ravel and reshape.
        flags = arr.flags
        flat = arr.ravel("K")
        result = astype_nansafe(flat, dtype, copy=copy, skipna=skipna)
        order = "F" if flags.f_contiguous else "C"
        return result.reshape(arr.shape, order=order)

    # We get here with 0-dim from sparse
    arr = np.atleast_1d(arr)

    # dispatch on extension dtype if needed
    if isinstance(dtype, ExtensionDtype):
        return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)

    elif not isinstance(dtype, np.dtype):
        raise ValueError("dtype must be np.dtype or ExtensionDtype")

    if arr.dtype.kind in ["m", "M"] and (
        issubclass(dtype.type, str) or dtype == object
    ):
        from pandas.core.construction import ensure_wrapped_if_datetimelike

        arr = ensure_wrapped_if_datetimelike(arr)
        return arr.astype(dtype, copy=copy)

    if issubclass(dtype.type, str):
        return lib.ensure_string_array(arr, skipna=skipna, convert_na_value=False)

    elif is_datetime64_dtype(arr):
        if dtype == np.int64:
            warnings.warn(
                f"casting {arr.dtype} values to int64 with .astype(...) "
                "is deprecated and will raise in a future version. "
                "Use .view(...) instead.",
                FutureWarning,
                # stacklevel chosen to be correct when reached via Series.astype
                stacklevel=7,
            )
            if isna(arr).any():
                raise ValueError("Cannot convert NaT values to integer")
            return arr.view(dtype)

        # allow frequency conversions
        if dtype.kind == "M":
            return arr.astype(dtype)

        raise TypeError(f"cannot astype a datetimelike from [{arr.dtype}] to [{dtype}]")

    elif is_timedelta64_dtype(arr):
        if dtype == np.int64:
            warnings.warn(
                f"casting {arr.dtype} values to int64 with .astype(...) "
                "is deprecated and will raise in a future version. "
                "Use .view(...) instead.",
                FutureWarning,
                # stacklevel chosen to be correct when reached via Series.astype
                stacklevel=7,
            )
            if isna(arr).any():
                raise ValueError("Cannot convert NaT values to integer")
            return arr.view(dtype)

        elif dtype.kind == "m":
            return astype_td64_unit_conversion(arr, dtype, copy=copy)

        raise TypeError(f"cannot astype a timedelta from [{arr.dtype}] to [{dtype}]")

    elif np.issubdtype(arr.dtype, np.floating) and np.issubdtype(dtype, np.integer):
        if not np.isfinite(arr).all():
            raise ValueError("Cannot convert non-finite values (NA or inf) to integer")

    elif is_object_dtype(arr):
        # work around NumPy brokenness, #1987
        if np.issubdtype(dtype.type, np.integer):
            return lib.astype_intsafe(arr, dtype)

        # if we have a datetime/timedelta array of objects
        # then coerce to a proper dtype and recall astype_nansafe

        elif is_datetime64_dtype(dtype):
            from pandas import to_datetime

            return astype_nansafe(to_datetime(arr).values, dtype, copy=copy)
        elif is_timedelta64_dtype(dtype):
            from pandas import to_timedelta

            return astype_nansafe(to_timedelta(arr)._values, dtype, copy=copy)

    if dtype.name in ("datetime64", "timedelta64"):
        msg = (
            f"The '{dtype.name}' dtype has no unit. Please pass in "
            f"'{dtype.name}[ns]' instead."
        )
        raise ValueError(msg)

    if copy or is_object_dtype(arr) or is_object_dtype(dtype):
        # Explicit copy, or required since NumPy can't view from / to object.
        return arr.astype(dtype, copy=True)

    return arr.view(dtype)