# Buggy code
def _get_with(self, key):
    # other: fancy integer or otherwise
    if isinstance(key, slice):
        # _convert_slice_indexer to determin if this slice is positional
        #  or label based, and if the latter, convert to positional
        slobj = self.index._convert_slice_indexer(key, kind="getitem")
        return self._slice(slobj)
    elif isinstance(key, ABCDataFrame):
        raise TypeError(
            "Indexing a Series with DataFrame is not "
            "supported, use the appropriate DataFrame column"
        )
    elif isinstance(key, tuple):
        return self._get_values_tuple(key)

    elif not is_list_like(key):
        # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684
        return self.loc[key]

    if not isinstance(key, (list, np.ndarray, ExtensionArray, Series, Index)):
        key = list(key)

    if isinstance(key, Index):
        key_type = key.inferred_type
    else:
        key_type = lib.infer_dtype(key, skipna=False)

    # Note: The key_type == "boolean" case should be caught by the
    #  com.is_bool_indexer check in __getitem__
    if key_type == "integer":
        # We need to decide whether to treat this as a positional indexer
        #  (i.e. self.iloc) or label-based (i.e. self.loc)
        if not self.index._should_fallback_to_positional():
            return self.loc[key]
        else:
            return self.iloc[key]

    if isinstance(key, list):
        # handle the dup indexing case GH#4246
        return self.loc[key]

    return self.reindex(key)


# Traceback (KeyError)
ser = pd.Series(["A", "B"])
key = pd.Series(["C"])

ser[key]
C    NaN
dtype: object

ser[pd.Index(key)]
C    NaN
dtype: object

ser[np.array(key)]
C    NaN
dtype: object

ser[list(key)]
Traceback (most recent call last):
[...]
File "/Users/bmendel/Desktop/pd/pandas/pandas/core/indexing.py", line 1312, in _validate_read_indexer
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['C'], dtype='object')] are in the [index]"

# Fixed code
def _get_with(self, key):
    # other: fancy integer or otherwise
    if isinstance(key, slice):
        # _convert_slice_indexer to determin if this slice is positional
        #  or label based, and if the latter, convert to positional
        slobj = self.index._convert_slice_indexer(key, kind="getitem")
        return self._slice(slobj)
    elif isinstance(key, ABCDataFrame):
        raise TypeError(
            "Indexing a Series with DataFrame is not "
            "supported, use the appropriate DataFrame column"
        )
    elif isinstance(key, tuple):
        return self._get_values_tuple(key)

    elif not is_list_like(key):
        # e.g. scalars that aren't recognized by lib.is_scalar, GH#32684
        return self.loc[key]

    if not isinstance(key, (list, np.ndarray, ExtensionArray, Series, Index)):
        key = list(key)

    if isinstance(key, Index):
        key_type = key.inferred_type
    else:
        key_type = lib.infer_dtype(key, skipna=False)

    # Note: The key_type == "boolean" case should be caught by the
    #  com.is_bool_indexer check in __getitem__
    if key_type == "integer":
        # We need to decide whether to treat this as a positional indexer
        #  (i.e. self.iloc) or label-based (i.e. self.loc)
        if not self.index._should_fallback_to_positional():
            return self.loc[key]
        else:
            return self.iloc[key]

    # handle the dup indexing case GH#4246
    return self.loc[key]