files.new_contents: import numpy as np
  
  def arange(start, /, *, stop=None, step=1, dtype=None, device=None):
      if device is not None:
          # Note: Device support is not yet implemented on ndarray
          raise NotImplementedError("Device support is not yet implemented")
      return np.arange(start, stop=stop, step=step, dtype=dtype)
  
  def empty(shape, /, *, dtype=None, device=None):
      if device is not None:
          # Note: Device support is not yet implemented on ndarray
          raise NotImplementedError("Device support is not yet implemented")
      return np.empty(shape, dtype=dtype)
  
  def empty_like(x, /, *, dtype=None, device=None):
      if device is not None:
          # Note: Device support is not yet implemented on ndarray
          raise NotImplementedError("Device support is not yet implemented")
      return np.empty_like(x, dtype=dtype)
  
  def eye(N, /, *, M=None, k=0, dtype=None, device=None):
      if device is not None
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_creation_functions.py
files.old_contents: def arange(start, /, *, stop=None, step=1, dtype=None, device=None):
      from .. import arange
      if device is not None:
          # Note: Device support is not yet implemented on ndarray
          raise NotImplementedError("Device support is not yet implemented")
      return arange(start, stop=stop, step=step, dtype=dtype)
  
  def empty(shape, /, *, dtype=None, device=None):
      from .. import empty
      if device is not None:
          # Note: Device support is not yet implemented on ndarray
          raise NotImplementedError("Device support is not yet implemented")
      return empty(shape, dtype=dtype)
  
  def empty_like(x, /, *, dtype=None, device=None):
      from .. import empty_like
      if device is not None:
          # Note: Device support is not yet implemented on ndarray
          raise NotImplementedError("Device support is not yet implemented")
      return empty_like(x, dtype=dtype)
  
  def eye(N, /, *, M=None, k=
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_creation_functions.py
files.new_contents: import numpy as np
  
  def abs(x, /):
      return np.abs(x)
  
  def acos(x, /):
      # Note: the function name is different here
      return np.arccos(x)
  
  def acosh(x, /):
      # Note: the function name is different here
      return np.arccosh(x)
  
  def add(x1, x2, /):
      return np.add(x1, x2)
  
  def asin(x, /):
      # Note: the function name is different here
      return np.arcsin(x)
  
  def asinh(x, /):
      # Note: the function name is different here
      return np.arcsinh(x)
  
  def atan(x, /):
      # Note: the function name is different here
      return np.arctan(x)
  
  def atan2(x1, x2, /):
      # Note: the function name is different here
      return np.arctan2(x1, x2)
  
  def atanh(x, /):
      # Note: the function name is different here
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_elementwise_functions.py
files.old_contents: def abs(x, /):
      from .. import abs
      return abs(x)
  
  def acos(x, /):
      # Note: the function name is different here
      from .. import arccos
      return arccos(x)
  
  def acosh(x, /):
      # Note: the function name is different here
      from .. import arccosh
      return arccosh(x)
  
  def add(x1, x2, /):
      from .. import add
      return add(x1, x2)
  
  def asin(x, /):
      # Note: the function name is different here
      from .. import arcsin
      return arcsin(x)
  
  def asinh(x, /):
      # Note: the function name is different here
      from .. import arcsinh
      return arcsinh(x)
  
  def atan(x, /):
      # Note: the function name is different here
      from .. import arctan
      return arctan(x)
  
  def atan2(x1, x2, /):
      # Note:
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_elementwise_functions.py
files.new_contents: import numpy as np
  
  # def cholesky():
  #     return np.cholesky()
  
  def cross(x1, x2, /, *, axis=-1):
      return np.cross(x1, x2, axis=axis)
  
  def det(x, /):
      # Note: this function is being imported from a nondefault namespace
      return np.det(x)
  
  def diagonal(x, /, *, axis1=0, axis2=1, offset=0):
      return np.diagonal(x, axis1=axis1, axis2=axis2, offset=offset)
  
  # def dot():
  #     return np.dot()
  #
  # def eig():
  #     return np.eig()
  #
  # def eigvalsh():
  #     return np.eigvalsh()
  #
  # def einsum():
  #     return np.einsum()
  
  def inv(x):
      # Note: this function is being imported from a nondefault namespace
      return np.inv(x)
  
  # def lstsq():
  #     return np.lstsq()
  #
  # def matmul():
  #    
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_linear_algebra_functions.py
files.old_contents: # def cholesky():
  #     from .. import cholesky
  #     return cholesky()
  
  def cross(x1, x2, /, *, axis=-1):
      from .. import cross
      return cross(x1, x2, axis=axis)
  
  def det(x, /):
      # Note: this function is being imported from a nondefault namespace
      from ..linalg import det
      return det(x)
  
  def diagonal(x, /, *, axis1=0, axis2=1, offset=0):
      from .. import diagonal
      return diagonal(x, axis1=axis1, axis2=axis2, offset=offset)
  
  # def dot():
  #     from .. import dot
  #     return dot()
  #
  # def eig():
  #     from .. import eig
  #     return eig()
  #
  # def eigvalsh():
  #     from .. import eigvalsh
  #     return eigvalsh()
  #
  # def einsum():
  #     from .. import einsum
  #     return einsum()
  
  def inv(x):
      #
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_linear_algebra_functions.py
files.new_contents: import numpy as np
  
  def concat(arrays, /, *, axis=0):
      # Note: the function name is different here
      return np.concatenate(arrays, axis=axis)
  
  def expand_dims(x, axis, /):
      return np.expand_dims(x, axis)
  
  def flip(x, /, *, axis=None):
      return np.flip(x, axis=axis)
  
  def reshape(x, shape, /):
      return np.reshape(x, shape)
  
  def roll(x, shift, /, *, axis=None):
      return np.roll(x, shift, axis=axis)
  
  def squeeze(x, /, *, axis=None):
      return np.squeeze(x, axis=axis)
  
  def stack(arrays, /, *, axis=0):
      return np.stack(arrays, axis=axis)
  
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_manipulation_functions.py
files.old_contents: def concat(arrays, /, *, axis=0):
      # Note: the function name is different here
      from .. import concatenate
      return concatenate(arrays, axis=axis)
  
  def expand_dims(x, axis, /):
      from .. import expand_dims
      return expand_dims(x, axis)
  
  def flip(x, /, *, axis=None):
      from .. import flip
      return flip(x, axis=axis)
  
  def reshape(x, shape, /):
      from .. import reshape
      return reshape(x, shape)
  
  def roll(x, shift, /, *, axis=None):
      from .. import roll
      return roll(x, shift, axis=axis)
  
  def squeeze(x, /, *, axis=None):
      from .. import squeeze
      return squeeze(x, axis=axis)
  
  def stack(arrays, /, *, axis=0):
      from .. import stack
      return stack(arrays, axis=axis)
  
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_manipulation_functions.py
files.new_contents: import numpy as np
  
  def argmax(x, /, *, axis=None, keepdims=False):
      return np.argmax(x, axis=axis, keepdims=keepdims)
  
  def argmin(x, /, *, axis=None, keepdims=False):
      return np.argmin(x, axis=axis, keepdims=keepdims)
  
  def nonzero(x, /):
      return np.nonzero(x)
  
  def where(condition, x1, x2, /):
      return np.where(condition, x1, x2)
  
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_searching_functions.py
files.old_contents: def argmax(x, /, *, axis=None, keepdims=False):
      from .. import argmax
      return argmax(x, axis=axis, keepdims=keepdims)
  
  def argmin(x, /, *, axis=None, keepdims=False):
      from .. import argmin
      return argmin(x, axis=axis, keepdims=keepdims)
  
  def nonzero(x, /):
      from .. import nonzero
      return nonzero(x)
  
  def where(condition, x1, x2, /):
      from .. import where
      return where(condition, x1, x2)
  
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_searching_functions.py
files.new_contents: import numpy as np
  
  def unique(x, /, *, return_counts=False, return_index=False, return_inverse=False, sorted=True):
      return np.unique(x, return_counts=return_counts, return_index=return_index, return_inverse=return_inverse, sorted=sorted)
  
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_set_functions.py
files.old_contents: def unique(x, /, *, return_counts=False, return_index=False, return_inverse=False, sorted=True):
      from .. import unique
      return unique(x, return_counts=return_counts, return_index=return_index, return_inverse=return_inverse, sorted=sorted)
  
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_set_functions.py
files.new_contents: import numpy as np
  
  def argsort(x, /, *, axis=-1, descending=False, stable=True):
      # Note: this keyword argument is different, and the default is different.
      kind = 'stable' if stable else 'quicksort'
      res = np.argsort(x, axis=axis, kind=kind)
      if descending:
          res = np.flip(res, axis=axis)
      return res
  
  def sort(x, /, *, axis=-1, descending=False, stable=True):
      # Note: this keyword argument is different, and the default is different.
      kind = 'stable' if stable else 'quicksort'
      res = np.sort(x, axis=axis, kind=kind)
      if descending:
          res = np.flip(res, axis=axis)
      return res
  
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_sorting_functions.py
files.old_contents: def argsort(x, /, *, axis=-1, descending=False, stable=True):
      from .. import argsort
      from .. import flip
      # Note: this keyword argument is different, and the default is different.
      kind = 'stable' if stable else 'quicksort'
      res = argsort(x, axis=axis, kind=kind)
      if descending:
          res = flip(res, axis=axis)
      return res
  
  def sort(x, /, *, axis=-1, descending=False, stable=True):
      from .. import sort
      from .. import flip
      # Note: this keyword argument is different, and the default is different.
      kind = 'stable' if stable else 'quicksort'
      res = sort(x, axis=axis, kind=kind)
      if descending:
          res = flip(res, axis=axis)
      return res
  
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_sorting_functions.py
files.new_contents: import numpy as np
  
  def max(x, /, *, axis=None, keepdims=False):
      return np.max(x, axis=axis, keepdims=keepdims)
  
  def mean(x, /, *, axis=None, keepdims=False):
      return np.mean(x, axis=axis, keepdims=keepdims)
  
  def min(x, /, *, axis=None, keepdims=False):
      return np.min(x, axis=axis, keepdims=keepdims)
  
  def prod(x, /, *, axis=None, keepdims=False):
      return np.prod(x, axis=axis, keepdims=keepdims)
  
  def std(x, /, *, axis=None, correction=0.0, keepdims=False):
      # Note: the keyword argument correction is different here
      return np.std(x, axis=axis, ddof=correction, keepdims=keepdims)
  
  def sum(x, /, *, axis=None, keepdims=False):
      return np.sum(x, axis=axis, keep
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_statistical_functions.py
files.old_contents: def max(x, /, *, axis=None, keepdims=False):
      from .. import max
      return max(x, axis=axis, keepdims=keepdims)
  
  def mean(x, /, *, axis=None, keepdims=False):
      from .. import mean
      return mean(x, axis=axis, keepdims=keepdims)
  
  def min(x, /, *, axis=None, keepdims=False):
      from .. import min
      return min(x, axis=axis, keepdims=keepdims)
  
  def prod(x, /, *, axis=None, keepdims=False):
      from .. import prod
      return prod(x, axis=axis, keepdims=keepdims)
  
  def std(x, /, *, axis=None, correction=0.0, keepdims=False):
      from .. import std
      # Note: the keyword argument correction is different here
      return std(x, axis=axis, ddof=correction, keepdims=keepdims)
  
  def sum(x, /, *,
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_statistical_functions.py
files.new_contents: import numpy as np
  
  def all(x, /, *, axis=None, keepdims=False):
      return np.all(x, axis=axis, keepdims=keepdims)
  
  def any(x, /, *, axis=None, keepdims=False):
      return np.any(x, axis=axis, keepdims=keepdims)
  
  
  [... the rest is omitted ...]
files.new_path: numpy/_array_api/_utility_functions.py
files.old_contents: def all(x, /, *, axis=None, keepdims=False):
      from .. import all
      return all(x, axis=axis, keepdims=keepdims)
  
  def any(x, /, *, axis=None, keepdims=False):
      from .. import any
      return any(x, axis=axis, keepdims=keepdims)
  
  
  [... the rest is omitted ...]
files.old_path: numpy/_array_api/_utility_functions.py