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 35  


 36  
r �	ArrayLikeztyping.Union[...]ag
 37      A `~typing.Union` representing objects that can be coerced
 38      into an `~numpy.ndarray`.
 39  
 40      Among others this includes the likes of:
 41  
 42      * Scalars.
 43      * (Nested) sequences.
 44      * Objects implementing the `~class.__array__` protocol.
 45  
 46      .. versionadded:: 1.20
 47  
 48      See Also
 49      --------
 50      :term:`array_like`:
 51          Any scalar or sequence that can be interpreted as an ndarray.
 52  
 53      Examples
 54      --------
 55      .. code-block:: python
 56  
 57          >>> import numpy as np
 58          >>> import numpy.typing as npt
 59  
 60          >>> def as_array(a: npt.ArrayLike) -> np.ndarray:
 61          ...     return np.array(a)
 62  
 63      �	DTypeLikea�
 64      A `~typing.Union` representing objects that can be coerced
 65      into a `~numpy.dtype`.
 66  
 67      Among others this includes the likes of:
 68  
 69      * :class:`type` objects.
 70      * Character codes or the names of :class:`type` objects.
 71      * Objects with the ``.dtype`` attribute.
 72  
 73      .. versionadded:: 1.20
 74  
 75      See Also
 76      --------
 77      :ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
 78          A comprehensive overview of all objects that can be coerced
 79          into data types.
 80  
 81      Examples
 82      --------
 83      .. code-block:: python
 84  
 85          >>> import numpy as np
 86          >>> import numpy.typing as npt
 87  
 88          >>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
 89          ...     return np.dtype(d)
 90  
 91      ra�
 92      A :term:`generic <generic type>` version of
 93      `np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
 94  
 95      Can be used during runtime for typing arrays with a given dtype
 96      and unspecified shape.
 97  
 98      .. versionadded:: 1.21
 99  
100      Examples
101      --------
102      .. code-block:: python
103  
104          >>> import numpy as np
105          >>> import numpy.typing as npt
106  
107          >>> print(npt.NDArray)
108          numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
109  
110          >>> print(npt.NDArray[np.float64])
111          numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
112  
113          >>> NDArrayInt = npt.NDArray[np.int_]
114          >>> a: NDArrayInt = np.arange(10)
115  
116          >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
117          ...     return np.array(a)
118  
119      )�__doc__rr�_generic_aliasrr�strrr �repr�_docstringsr
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