class_docstr: A pandas ExtensionArray for NumPy data.
  
  This is mostly for internal compatibility, and is not especially
  useful on its own.
  
  Parameters
  ----------
  values : ndarray
      The NumPy ndarray to wrap. Must be 1-dimensional.
  copy : bool, default False
      Whether to copy `values`.
  
  Attributes
  ----------
  None
  
  Methods
  -------
  None
  
  Examples
  --------
  >>> pd.arrays.NumpyExtensionArray(np.array([0, 1, 2, 3]))
  <NumpyExtensionArray>
  [0, 1, 2, 3]
  Length: 4, dtype: int64
code: def __array_ufunc__(
      self, ufunc: np.ufunc, method: str, *inputs, **kwargs
  ):
      # Lightly modified version of
      # https://numpy.org/doc/stable/reference/generated/numpy.lib.mixins.NDArrayOperatorsMixin.html
      # The primary modification is not boxing scalar return values
      # in NumpyExtensionArray, since pandas' ExtensionArrays are 1-d.
      out = kwargs.get("out", ())
  
      result = arraylike.maybe_dispatch_ufunc_to_dunder_op(
          self, ufunc, method, *inputs, **kwargs
      )
      if result is not NotImplemented:
          return result
  
      if "out" in kwargs:
          # e.g. test_ufunc_unary
          return arraylike.dispatch_ufunc_with_out(
              self, ufunc, method, *inputs, **kwargs
          )
  
      if method == "reduce":
          result = arraylike.dispatch_reduction_ufunc(
              self, ufunc, method, *inputs, **kwargs
          )
          if result is not NotImplemented:
              # e.g. tests.series.test_ufunc.TestNumpyReductions
              return result
  
      # Defer to the implementation of the ufunc on unwrapped values.
      inputs = tuple(
          x._ndarray if isinstance(x, NumpyExtensionArray) else x
          for x in inputs
      )
      if out:
          kwargs["out"] = tuple(
              x._ndarray if isinstance(x, NumpyExtensionArray) else x
              for x in out
          )
      result = getattr(ufunc, method)(*inputs, **kwargs)
  
      if ufunc.nout > 1:
          # multiple return values; re-box array-like results
          return tuple(type(self)(x) for x in result)
      elif method == "at":
          # no return value
          return None
      elif method == "reduce":
          if isinstance(result, np.ndarray):
              # e.g. test_np_reduce_2d
              return type(self)(result)
  
          # e.g. test_np_max_nested_tuples
          return result
      else:
          # one return value; re-box array-like results
          return type(self)(result)
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Literal
  import numpy
  from pandas._libs import lib
  from pandas._libs.tslibs import is_supported_dtype
  from pandas.compat.numpy import function
  from pandas.core.dtypes.astype import astype_array
  from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
  from pandas.core.dtypes.common import pandas_dtype
  from pandas.core.dtypes.dtypes import NumpyEADtype
  from pandas.core.dtypes.missing import isna
  from pandas.core import arraylike, missing, nanops, ops
  from pandas.core.arraylike import OpsMixin
  from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
  from pandas.core.construction import ensure_wrapped_if_datetimelike
  from pandas.core.strings.object_array import ObjectStringArrayMixin
label_desc: Use image node_id 6 for calling the NumpyExtensionArray obj's underlying member method code with example usage: obj.__array_ufunc__(ufunc, method) and returns: result, arraylike, tuple, result, None, result, type, type