class_docstr: Abstract base class for custom 1-D array types.
  
  pandas will recognize instances of this class as proper arrays
  with a custom type and will not attempt to coerce them to objects. They
  may be stored directly inside a :class:`DataFrame` or :class:`Series`.
  
  .. versionadded:: 0.23.0
  
  Notes
  -----
  The interface includes the following abstract methods that must be
  implemented by subclasses:
  
  * _from_sequence
  * _from_factorized
  * __getitem__
  * __len__
  * dtype
  * nbytes
  * isna
  * take
  * copy
  * _concat_same_type
  
  A default repr displaying the type, (truncated) data, length,
  and dtype is provided. It can be customized or replaced by
  by overriding:
  
  * __repr__ : A default repr for the ExtensionArray.
  * _formatter : Print scalars inside a Series or DataFrame.
  
  Some methods require casting the ExtensionArray to an ndarray of Python
  objects with ``self.astype(object)``, which may be expensive. When
  performance is a concern, we highly recommend overriding the following
  methods:
  
  * fillna
  * dropna
  * unique
  * factorize / _values_for_factorize
  * argsort / _values_for_argsort
  * searchsorted
  
  The remaining methods implemented on this class should be performant,
  as they only compose abstract methods. Still, a more efficient
  implementation may be available, and these methods can be overridden.
  
  One can implement methods to handle array reductions.
  
  * _reduce
  
  One can implement methods to handle parsing from strings that will be used
  in methods such as ``pandas.io.parsers.read_csv``.
  
  * _from_sequence_of_strings
  
  This class does not inherit from 'abc.ABCMeta' for performance reasons.
  Methods and properties required by the interface raise
  ``pandas.errors.AbstractMethodError`` and no ``register`` method is
  provided for registering virtual subclasses.
  
  ExtensionArrays are limited to 1 dimension.
  
  They may be backed by none, one, or many NumPy arrays. For example,
  ``pandas.Categorical`` is an extension array backed by two arrays,
  one for codes and one for categories. An array of IPv6 address may
  be backed by a NumPy structured array with two fields, one for the
  lower 64 bits and one for the upper 64 bits. Or they may be backed
  by some other storage type, like Python lists. Pandas makes no
  assumptions on how the data are stored, just that it can be converted
  to a NumPy array.
  The ExtensionArray interface does not impose any rules on how this data
  is stored. However, currently, the backing data cannot be stored in
  attributes called ``.values`` or ``._values`` to ensure full compatibility
  with pandas internals. But other names as ``.data``, ``._data``,
  ``._items``, ... can be freely used.
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docstr: Set one or more values inplace.
  
  This method is not required to satisfy the pandas extension array
  interface.
  
  Parameters
  ----------
  key : int, ndarray, or slice
      When called from, e.g. ``Series.__setitem__``, ``key`` will be
      one of
  
      * scalar int
      * ndarray of integers.
      * boolean ndarray
      * slice object
  
  value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
      value or values to be set of ``key``.
  
  Returns
  -------
  None
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code: def __setitem__(self, key, value):
      # type: (Union[int, np.ndarray], Any) -> None
      """
      Set one or more values inplace.
  
      This method is not required to satisfy the pandas extension array
      interface.
  
      Parameters
      ----------
      key : int, ndarray, or slice
          When called from, e.g. ``Series.__setitem__``, ``key`` will be
          one of
  
          * scalar int
          * ndarray of integers.
          * boolean ndarray
          * slice object
  
      value : ExtensionDtype.type, Sequence[ExtensionDtype.type], or object
          value or values to be set of ``key``.
  
      Returns
      -------
      None
      """
      # Some notes to the ExtensionArray implementor who may have ended up
      # here. While this method is not required for the interface, if you
      # *do* choose to implement __setitem__, then some semantics should be
      # observed:
      #
      # * Setting multiple values : ExtensionArrays should support setting
      #   multiple values at once, 'key' will be a sequence of integers and
      #  'value' will be a same-length sequence.
      #
      # * Broadcasting : For a sequence 'key' and a scalar 'value',
      #   each position in 'key' should be set to 'value'.
      #
      # * Coercion : Most users will expect basic coercion to work. For
      #   example, a string like '2018-01-01' is coerced to a datetime
      #   when setting on a datetime64ns array. In general, if the
      #   __init__ method coerces that value, then so should __setitem__
      # Note, also, that Series/DataFrame.where internally use __setitem__
      # on a copy of the data.
      raise NotImplementedError(
          _not_implemented_message.format(type(self), "__setitem__")
      )
  
imports: import operator
  import numpy
  from pandas.compat import PY3, set_function_name
  from pandas.compat.numpy import function
  from pandas.errors import AbstractMethodError
  from pandas.util._decorators import Appender, Substitution
  from pandas.core.dtypes.common import is_list_like
  from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
  from pandas.core.dtypes.missing import isna
  from pandas.core import ops
label_desc: Use image node_id 5 for calling the ExtensionArray obj's underlying member method code with example usage: obj.__setitem__(key, value) without return types