# Buggy code
def _try_cast(
    arr,
    dtype: Optional[DtypeObj],
    copy: bool,
    raise_cast_failure: bool,
):
    """
    Convert input to numpy ndarray and optionally cast to a given dtype.

    Parameters
    ----------
    arr : ndarray, list, tuple, iterator (catchall)
        Excludes: ExtensionArray, Series, Index.
    dtype : np.dtype, ExtensionDtype or None
    copy : bool
        If False, don't copy the data if not needed.
    raise_cast_failure : bool
        If True, and if a dtype is specified, raise errors during casting.
        Otherwise an object array is returned.
    """
    # perf shortcut as this is the most common case
    if isinstance(arr, np.ndarray):
        if maybe_castable(arr) and not copy and dtype is None:
            return arr

    if isinstance(dtype, ExtensionDtype) and dtype.kind != "M":
        # create an extension array from its dtype
        # DatetimeTZ case needs to go through maybe_cast_to_datetime
        array_type = dtype.construct_array_type()._from_sequence
        subarr = array_type(arr, dtype=dtype, copy=copy)
        return subarr

    try:
        # GH#15832: Check if we are requesting a numeric dype and
        # that we can convert the data to the requested dtype.
        if is_integer_dtype(dtype):
            # this will raise if we have e.g. floats
            maybe_cast_to_integer_array(arr, dtype)
            subarr = arr
        else:
            subarr = maybe_cast_to_datetime(arr, dtype)

        # Take care in creating object arrays (but iterators are not
        # supported):
        if is_object_dtype(dtype) and (
            is_list_like(subarr)
            and not (is_iterator(subarr) or isinstance(subarr, np.ndarray))
        ):
            subarr = construct_1d_object_array_from_listlike(subarr)
        elif not is_extension_array_dtype(subarr):
            subarr = construct_1d_ndarray_preserving_na(subarr, dtype, copy=copy)
    except OutOfBoundsDatetime:
        # in case of out of bound datetime64 -> always raise
        raise
    except (ValueError, TypeError):
        if dtype is not None and raise_cast_failure:
            raise
        else:
            subarr = np.array(arr, dtype=object, copy=copy)
    return subarr


# Traceback (TypeError)
In [8]: pd.Series(None, index=[1, 2, 3], dtype='Int64')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/scipy/pandas/pandas/core/internals/construction.py in _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure)
691         if is_integer_dtype(dtype):
--> 692             subarr = maybe_cast_to_integer_array(arr, dtype)
693

~/scipy/pandas/pandas/core/dtypes/cast.py in maybe_cast_to_integer_array(arr, dtype, copy)
1311         if not hasattr(arr, "astype"):
-> 1312             casted = np.array(arr, dtype=dtype, copy=copy)
1313         else:

TypeError: data type not understood

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
<ipython-input-8-9447295feee6> in <module>
----> 1 pd.Series(None, index=[1, 2, 3], dtype='Int64')

~/scipy/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
202                 data = data._data
203             elif isinstance(data, dict):
--> 204                 data, index = self._init_dict(data, index, dtype)
205                 dtype = None
206                 copy = False

~/scipy/pandas/pandas/core/series.py in _init_dict(self, data, index, dtype)
295
296         # Input is now list-like, so rely on "standard" construction:
--> 297         s = Series(values, index=keys, dtype=dtype)
298
299         # Now we just make sure the order is respected, if any

~/scipy/pandas/pandas/core/series.py in __init__(self, data, index, dtype, name, copy, fastpath)
253             else:
254                 data = sanitize_array(data, index, dtype, copy,
--> 255                                       raise_cast_failure=True)
256
257                 data = SingleBlockManager(data, index, fastpath=True)

~/scipy/pandas/pandas/core/internals/construction.py in sanitize_array(data, index, dtype, copy, raise_cast_failure)
620         subarr = _try_cast(arr, False, dtype, copy, raise_cast_failure)
621     else:
--> 622         subarr = _try_cast(data, False, dtype, copy, raise_cast_failure)
623
624     # scalar like, GH

~/scipy/pandas/pandas/core/internals/construction.py in _try_cast(arr, take_fast_path, dtype, copy, raise_cast_failure)
711             # create an extension array from its dtype
712             array_type = dtype.construct_array_type()._from_sequence
--> 713             subarr = array_type(arr, dtype=dtype, copy=copy)
714         elif dtype is not None and raise_cast_failure:
715             raise

~/scipy/pandas/pandas/core/arrays/integer.py in _from_sequence(cls, scalars, dtype, copy)
305     @classmethod
306     def _from_sequence(cls, scalars, dtype=None, copy=False):
--> 307         return integer_array(scalars, dtype=dtype, copy=copy)
308
309     @classmethod

~/scipy/pandas/pandas/core/arrays/integer.py in integer_array(values, dtype, copy)
110     TypeError if incompatible types
111     """
--> 112     values, mask = coerce_to_array(values, dtype=dtype, copy=copy)
113     return IntegerArray(values, mask)
114

~/scipy/pandas/pandas/core/arrays/integer.py in coerce_to_array(values, dtype, mask, copy)
202
203     if not values.ndim == 1:
--> 204         raise TypeError("values must be a 1D list-like")
205     if not mask.ndim == 1:
206         raise TypeError("mask must be a 1D list-like")

TypeError: values must be a 1D list-like

# Fixed code
def _try_cast(
    arr,
    dtype: Optional[DtypeObj],
    copy: bool,
    raise_cast_failure: bool,
):
    """
    Convert input to numpy ndarray and optionally cast to a given dtype.

    Parameters
    ----------
    arr : ndarray, scalar, list, tuple, iterator (catchall)
        Excludes: ExtensionArray, Series, Index.
    dtype : np.dtype, ExtensionDtype or None
    copy : bool
        If False, don't copy the data if not needed.
    raise_cast_failure : bool
        If True, and if a dtype is specified, raise errors during casting.
        Otherwise an object array is returned.
    """
    # perf shortcut as this is the most common case
    if isinstance(arr, np.ndarray):
        if maybe_castable(arr) and not copy and dtype is None:
            return arr

    if isinstance(dtype, ExtensionDtype) and dtype.kind != "M":
        # create an extension array from its dtype
        # DatetimeTZ case needs to go through maybe_cast_to_datetime
        array_type = dtype.construct_array_type()._from_sequence
        subarr = array_type(arr, dtype=dtype, copy=copy)
        return subarr

    try:
        # GH#15832: Check if we are requesting a numeric dype and
        # that we can convert the data to the requested dtype.
        if is_integer_dtype(dtype):
            # this will raise if we have e.g. floats
            maybe_cast_to_integer_array(arr, dtype)
            subarr = arr
        else:
            subarr = maybe_cast_to_datetime(arr, dtype)

        # Take care in creating object arrays (but iterators are not
        # supported):
        if is_object_dtype(dtype) and (
            is_list_like(subarr)
            and not (is_iterator(subarr) or isinstance(subarr, np.ndarray))
        ):
            subarr = construct_1d_object_array_from_listlike(subarr)
        elif not is_extension_array_dtype(subarr):
            subarr = construct_1d_ndarray_preserving_na(subarr, dtype, copy=copy)
    except OutOfBoundsDatetime:
        # in case of out of bound datetime64 -> always raise
        raise
    except (ValueError, TypeError):
        if dtype is not None and raise_cast_failure:
            raise
        else:
            subarr = np.array(arr, dtype=object, copy=copy)
    return subarr