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from __future__ import annotations
from ._array_object import ndarray
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ._types import Optional, Tuple, Union, array
import numpy as np
# Note: the function name is different here
def concat(arrays: Tuple[array, ...], /, *, axis: Optional[int] = 0) -> array:
"""
Array API compatible wrapper for :py:func:`np.concatenate <numpy.concatenate>`.
See its docstring for more information.
"""
arrays = tuple(a._array for a in arrays)
return ndarray._new(np.concatenate(arrays, axis=axis))
def expand_dims(x: array, /, *, axis: int) -> array:
"""
Array API compatible wrapper for :py:func:`np.expand_dims <numpy.expand_dims>`.
See its docstring for more information.
"""
return ndarray._new(np.expand_dims(x._array, axis))
def flip(x: array, /, *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> array:
"""
Array API compatible wrapper for :py:func:`np.flip <numpy.flip>`.
See its docstring for more information.
"""
return ndarray._new(np.flip(x._array, axis=axis))
def reshape(x: array, /, shape: Tuple[int, ...]) -> array:
"""
Array API compatible wrapper for :py:func:`np.reshape <numpy.reshape>`.
See its docstring for more information.
"""
return ndarray._new(np.reshape(x._array, shape))
def roll(x: array, /, shift: Union[int, Tuple[int, ...]], *, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> array:
"""
Array API compatible wrapper for :py:func:`np.roll <numpy.roll>`.
See its docstring for more information.
"""
return ndarray._new(np.roll(x._array, shift, axis=axis))
def squeeze(x: array, /, axis: Optional[Union[int, Tuple[int, ...]]] = None) -> array:
"""
Array API compatible wrapper for :py:func:`np.squeeze <numpy.squeeze>`.
See its docstring for more information.
"""
return ndarray._new(np.squeeze(x._array, axis=axis))
def stack(arrays: Tuple[array, ...], /, *, axis: int = 0) -> array:
"""
Array API compatible wrapper for :py:func:`np.stack <numpy.stack>`.
See its docstring for more information.
"""
arrays = tuple(a._array for a in arrays)
return ndarray._new(np.stack(arrays, axis=axis))
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