old_contents: import os
  import sys
  
  
  if sys.version_info[0] < 3:
      _available = False
  else:
      try:
          from chainerx import _core
          _available = True
      except Exception:
          _available = False
  
  
  if _available:
      from numpy import dtype  # NOQA
      from numpy import (
          bool_, int8, int16, int32, int64, uint8, float16, float32, float64)  # NOQA
      all_dtypes = (
          bool_, int8, int16, int32, int64, uint8, float16, float32, float64)
  
      from chainerx._core import *  # NOQA
  
      from builtins import bool, int, float  # NOQA
  
      from chainerx import _device  # NOQA
  
      from chainerx.creation.from_data import asanyarray  # NOQA
      from chainerx.creation.from_data import fromfile  # NOQA
      from chainerx.creation.from_data import fromfunction  # NOQA
      from chainerx.creation.from_data import fromiter  # NOQA
      from chainerx.creation.from_data import fromstring  # NOQA
      from chainerx.creation.from_data import loadtxt  # NOQA
  
      from chainerx.manipulation.shape import ravel  # NOQA
  
      from chainerx.math.misc import clip  # NOQA
  
      from chainerx import random  # NOQA
  
      _global_context = _core.Context()
      _core.set_global_default_context(_global_context)
  
      # Implements ndarray methods in Python
      from chainerx import _ndarray
      _ndarray.populate()
  
      # Temporary workaround implementations that fall back to NumPy/CuPy's
      # respective functions.
      from chainerx import _fallback_workarounds
      _fallback_workarounds.populate()
  
      # Dynamically inject docstrings
      from chainerx import _docs
      _docs.set_docs()
  
      from chainerx import _cuda
      # Share memory pool with CuPy.
      if bool(int(os.getenv('CHAINERX_CUDA_CUPY_SHARE_ALLOCATOR', '0'))):
          _cuda.cupy_share_allocator()
  else:
      class ndarray(object):
  
          """Dummy class for type testing."""
  
          def __init__(self, *args, **kwargs):
              raise RuntimeError('chainerx is not available.')
  
  
  def is_available():
      return _available
  
new_contents: import os
  import sys
  
  
  if sys.version_info[0] < 3:
      _available = False
  else:
      try:
          from chainerx import _core
          _available = True
      except Exception:
          _available = False
  
  
  if _available:
      from numpy import dtype  # NOQA
      from numpy import (
          bool_, int8, int16, int32, int64, uint8, float16, float32, float64)  # NOQA
      all_dtypes = (
          bool_, int8, int16, int32, int64, uint8, float16, float32, float64)
  
      from chainerx._core import *  # NOQA
      from chainerx._core import _to_cupy  # NOQA
  
      from builtins import bool, int, float  # NOQA
  
      from chainerx import _device  # NOQA
  
      from chainerx.creation.from_data import asanyarray  # NOQA
      from chainerx.creation.from_data import fromfile  # NOQA
      from chainerx.creation.from_data import fromfunction  # NOQA
      from chainerx.creation.from_data import fromiter  # NOQA
      from chainerx.creation.from_data import fromstring  # NOQA
      from chainerx.creation.from_data import loadtxt  # NOQA
  
      from chainerx.manipulation.shape import ravel  # NOQA
  
      from chainerx.math.misc import clip  # NOQA
  
      from chainerx import random  # NOQA
  
      _global_context = _core.Context()
      _core.set_global_default_context(_global_context)
  
      # Implements ndarray methods in Python
      from chainerx import _ndarray
      _ndarray.populate()
  
      # Temporary workaround implementations that fall back to NumPy/CuPy's
      # respective functions.
      from chainerx import _fallback_workarounds
      _fallback_workarounds.populate()
  
      # Dynamically inject docstrings
      from chainerx import _docs
      _docs.set_docs()
  
      from chainerx import _cuda
      # Share memory pool with CuPy.
      if bool(int(os.getenv('CHAINERX_CUDA_CUPY_SHARE_ALLOCATOR', '0'))):
          _cuda.cupy_share_allocator()
  else:
      class ndarray(object):
  
          """Dummy class for type testing."""
  
          def __init__(self, *args, **kwargs):
              raise RuntimeError('chainerx is not available.')
  
  
  def is_available():
      return _available
  
subject: add _to_cupy explicitly