# Buggy code
def maybe_cast_to_datetime(value, dtype: Optional[DtypeObj]):
    """
    try to cast the array/value to a datetimelike dtype, converting float
    nan to iNaT
    """
    from pandas.core.tools.datetimes import to_datetime
    from pandas.core.tools.timedeltas import to_timedelta

    if dtype is not None:
        is_datetime64 = is_datetime64_dtype(dtype)
        is_datetime64tz = is_datetime64tz_dtype(dtype)
        is_timedelta64 = is_timedelta64_dtype(dtype)

        if is_datetime64 or is_datetime64tz or is_timedelta64:
            # Force the dtype if needed.
            msg = (
                f"The '{dtype.name}' dtype has no unit. "
                f"Please pass in '{dtype.name}[ns]' instead."
            )

            if is_datetime64:
                # unpack e.g. SparseDtype
                dtype = getattr(dtype, "subtype", dtype)
                if not is_dtype_equal(dtype, DT64NS_DTYPE):
                    # pandas supports dtype whose granularity is less than [ns]
                    # e.g., [ps], [fs], [as]
                    if dtype <= np.dtype("M8[ns]"):
                        if dtype.name == "datetime64":
                            raise ValueError(msg)
                        dtype = DT64NS_DTYPE
                    else:
                        raise TypeError(
                            f"cannot convert datetimelike to dtype [{dtype}]"
                        )
            elif is_datetime64tz:
                # our NaT doesn't support tz's
                # this will coerce to DatetimeIndex with
                # a matching dtype below
                if is_scalar(value) and isna(value):
                    value = [value]

            elif is_timedelta64 and not is_dtype_equal(dtype, TD64NS_DTYPE):
                # pandas supports dtype whose granularity is less than [ns]
                # e.g., [ps], [fs], [as]
                if dtype <= np.dtype("m8[ns]"):
                    if dtype.name == "timedelta64":
                        raise ValueError(msg)
                    dtype = TD64NS_DTYPE
                else:
                    raise TypeError(f"cannot convert timedeltalike to dtype [{dtype}]")

            if is_scalar(value):
                if value == iNaT or isna(value):
                    value = iNaT
            elif not is_sparse(value):
                value = np.array(value, copy=False)

                # have a scalar array-like (e.g. NaT)
                if value.ndim == 0:
                    value = iNaT

                # we have an array of datetime or timedeltas & nulls
                elif np.prod(value.shape) or not is_dtype_equal(value.dtype, dtype):
                    try:
                        if is_datetime64:
                            value = to_datetime(value, errors="raise")
                            # GH 25843: Remove tz information since the dtype
                            # didn't specify one
                            if value.tz is not None:
                                value = value.tz_localize(None)
                            value = value._values
                        elif is_datetime64tz:
                            # The string check can be removed once issue #13712
                            # is solved. String data that is passed with a
                            # datetime64tz is assumed to be naive which should
                            # be localized to the timezone.
                            is_dt_string = is_string_dtype(value.dtype)
                            value = to_datetime(value, errors="raise").array
                            if is_dt_string:
                                # Strings here are naive, so directly localize
                                value = value.tz_localize(dtype.tz)
                            else:
                                # Numeric values are UTC at this point,
                                # so localize and convert
                                value = value.tz_localize("UTC").tz_convert(dtype.tz)
                        elif is_timedelta64:
                            value = to_timedelta(value, errors="raise")._values
                    except OutOfBoundsDatetime:
                        raise
                    except (AttributeError, ValueError, TypeError):
                        pass

        # coerce datetimelike to object
        elif is_datetime64_dtype(
            getattr(value, "dtype", None)
        ) and not is_datetime64_dtype(dtype):
            if is_object_dtype(dtype):
                if value.dtype != DT64NS_DTYPE:
                    value = value.astype(DT64NS_DTYPE)
                ints = np.asarray(value).view("i8")
                return ints_to_pydatetime(ints)

            # we have a non-castable dtype that was passed
            raise TypeError(f"Cannot cast datetime64 to {dtype}")

    else:
        is_array = isinstance(value, np.ndarray)

        # catch a datetime/timedelta that is not of ns variety
        # and no coercion specified
        if is_array and value.dtype.kind in ["M", "m"]:
            dtype = value.dtype

            if dtype.kind == "M" and dtype != DT64NS_DTYPE:
                value = conversion.ensure_datetime64ns(value)

            elif dtype.kind == "m" and dtype != TD64NS_DTYPE:
                value = conversion.ensure_timedelta64ns(value)

        # only do this if we have an array and the dtype of the array is not
        # setup already we are not an integer/object, so don't bother with this
        # conversion
        elif not (
            is_array
            and not (
                issubclass(value.dtype.type, np.integer) or value.dtype == np.object_
            )
        ):
            value = maybe_infer_to_datetimelike(value)

    return value


# Traceback (AssertionError)
In [31]: import pandas as pd
...: import pandas._testing as tm
...:
...: td = pd.Timedelta(nanoseconds=500)
...: ser = pd.Series({"a": td})
...: expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
...:
...: tm.assert_series_equal(ser, expected)
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-31-a9c6a6312101> in <module>
6 expected = pd.Series(td, index=["a"], dtype="timedelta64[ns]")
7
----> 8 tm.assert_series_equal(ser, expected)

[... skipping hidden 1 frame]

~/repos/pandas/pandas/_testing.py in assert_extension_array_equal(left, right, check_dtype, index_values, check_less_precise, check_exact, rtol, atol)
1243         # Avoid slow object-dtype comparisons
1244         # np.asarray for case where we have a np.MaskedArray
-> 1245         assert_numpy_array_equal(
1246             np.asarray(left.asi8), np.asarray(right.asi8), index_values=index_values
1247         )

[... skipping hidden 1 frame]

~/repos/pandas/pandas/_testing.py in _raise(left, right, err_msg)
1155             diff = diff * 100.0 / left.size
1156             msg = f"{obj} values are different ({np.round(diff, 5)} %)"
-> 1157             raise_assert_detail(obj, msg, left, right, index_values=index_values)
1158
1159         raise AssertionError(err_msg)

~/repos/pandas/pandas/_testing.py in raise_assert_detail(obj, message, left, right, diff, index_values)
1085         msg += f"\n[diff]: {diff}"
1086
-> 1087     raise AssertionError(msg)
1088
1089

AssertionError: numpy array are different

numpy array values are different (100.0 %)
[index]: [a]
[left]:  [500]
[right]: [0]

# Fixed code
def maybe_cast_to_datetime(value, dtype: Optional[DtypeObj]):
    """
    try to cast the array/value to a datetimelike dtype, converting float
    nan to iNaT
    """
    from pandas.core.tools.datetimes import to_datetime
    from pandas.core.tools.timedeltas import to_timedelta

    if dtype is not None:
        is_datetime64 = is_datetime64_dtype(dtype)
        is_datetime64tz = is_datetime64tz_dtype(dtype)
        is_timedelta64 = is_timedelta64_dtype(dtype)

        if is_datetime64 or is_datetime64tz or is_timedelta64:
            # Force the dtype if needed.
            msg = (
                f"The '{dtype.name}' dtype has no unit. "
                f"Please pass in '{dtype.name}[ns]' instead."
            )

            if is_datetime64:
                # unpack e.g. SparseDtype
                dtype = getattr(dtype, "subtype", dtype)
                if not is_dtype_equal(dtype, DT64NS_DTYPE):
                    # pandas supports dtype whose granularity is less than [ns]
                    # e.g., [ps], [fs], [as]
                    if dtype <= np.dtype("M8[ns]"):
                        if dtype.name == "datetime64":
                            raise ValueError(msg)
                        dtype = DT64NS_DTYPE
                    else:
                        raise TypeError(
                            f"cannot convert datetimelike to dtype [{dtype}]"
                        )
            elif is_datetime64tz:
                # our NaT doesn't support tz's
                # this will coerce to DatetimeIndex with
                # a matching dtype below
                if is_scalar(value) and isna(value):
                    value = [value]

            elif is_timedelta64 and not is_dtype_equal(dtype, TD64NS_DTYPE):
                # pandas supports dtype whose granularity is less than [ns]
                # e.g., [ps], [fs], [as]
                if dtype <= np.dtype("m8[ns]"):
                    if dtype.name == "timedelta64":
                        raise ValueError(msg)
                    dtype = TD64NS_DTYPE
                else:
                    raise TypeError(f"cannot convert timedeltalike to dtype [{dtype}]")

            if is_scalar(value):
                value = maybe_unbox_datetimelike(value, dtype)
            elif not is_sparse(value):
                value = np.array(value, copy=False)

                # have a scalar array-like (e.g. NaT)
                if value.ndim == 0:
                    value = iNaT

                # we have an array of datetime or timedeltas & nulls
                elif np.prod(value.shape) or not is_dtype_equal(value.dtype, dtype):
                    try:
                        if is_datetime64:
                            value = to_datetime(value, errors="raise")
                            # GH 25843: Remove tz information since the dtype
                            # didn't specify one
                            if value.tz is not None:
                                value = value.tz_localize(None)
                            value = value._values
                        elif is_datetime64tz:
                            # The string check can be removed once issue #13712
                            # is solved. String data that is passed with a
                            # datetime64tz is assumed to be naive which should
                            # be localized to the timezone.
                            is_dt_string = is_string_dtype(value.dtype)
                            value = to_datetime(value, errors="raise").array
                            if is_dt_string:
                                # Strings here are naive, so directly localize
                                value = value.tz_localize(dtype.tz)
                            else:
                                # Numeric values are UTC at this point,
                                # so localize and convert
                                value = value.tz_localize("UTC").tz_convert(dtype.tz)
                        elif is_timedelta64:
                            value = to_timedelta(value, errors="raise")._values
                    except OutOfBoundsDatetime:
                        raise
                    except (AttributeError, ValueError, TypeError):
                        pass

        # coerce datetimelike to object
        elif is_datetime64_dtype(
            getattr(value, "dtype", None)
        ) and not is_datetime64_dtype(dtype):
            if is_object_dtype(dtype):
                if value.dtype != DT64NS_DTYPE:
                    value = value.astype(DT64NS_DTYPE)
                ints = np.asarray(value).view("i8")
                return ints_to_pydatetime(ints)

            # we have a non-castable dtype that was passed
            raise TypeError(f"Cannot cast datetime64 to {dtype}")

    else:
        is_array = isinstance(value, np.ndarray)

        # catch a datetime/timedelta that is not of ns variety
        # and no coercion specified
        if is_array and value.dtype.kind in ["M", "m"]:
            dtype = value.dtype

            if dtype.kind == "M" and dtype != DT64NS_DTYPE:
                value = conversion.ensure_datetime64ns(value)

            elif dtype.kind == "m" and dtype != TD64NS_DTYPE:
                value = conversion.ensure_timedelta64ns(value)

        # only do this if we have an array and the dtype of the array is not
        # setup already we are not an integer/object, so don't bother with this
        # conversion
        elif not (
            is_array
            and not (
                issubclass(value.dtype.type, np.integer) or value.dtype == np.object_
            )
        ):
            value = maybe_infer_to_datetimelike(value)

    return value