old_contents: # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from . import core
  from . import lazy
  from .interpreters import xla
  from .lib import xla_client
  from .lib import xla_bridge
  
  def to_dlpack(x: xla.DeviceArray):
    """Returns a DLPack tensor that encapsulates a DeviceArray `x`.
  
    Takes ownership of the contents of `x`; leaves `x` in an invalid/deleted
    state.
  
    Args:
      x: a `DeviceArray`, on either CPU or GPU.
    """
    if not isinstance(x, xla.DeviceArray):
      raise TypeError("Argument to to_dlpack must be a DeviceArray, got {}"
                      .format(type(x)))
    buf = xla._force(x).device_buffer
    return xla_client._xla.BufferToDLPackManagedTensor(buf)
  
  def from_dlpack(dlpack, backend=None):
    """Returns a `DeviceArray` representation of a DLPack tensor `dlpack`.
  
    The returned `DeviceArray` shares memory with `dlpack`.
  
    Args:
      dlpack: a DLPack tensor, on either CPU or GPU.
      backend: experimental, optional: the platform on which `dlpack` lives.
    """
    # TODO(phawkins): ideally the user wouldn't need to provide a backend and we
    # would be able to figure it out from the DLPack.
    backend = backend or xla_bridge.get_backend()
    buf = xla_client._xla.DLPackManagedTensorToBuffer(dlpack, backend.client)
    xla_shape = buf.shape()
    assert not xla_shape.is_tuple()
    aval = core.ShapedArray(xla_shape.dimensions(), xla_shape.numpy_dtype())
    return xla.DeviceArray(aval, buf.device(), lazy.array(aval.shape), buf)  # pytype: disable=attribute-error
  
new_contents: # Copyright 2020 Google LLC
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from . import core
  from . import lazy
  from .interpreters import xla
  from .lib import xla_client
  from .lib import xla_bridge
  
  def to_dlpack(x: xla.DeviceArray):
    """Returns a DLPack tensor that encapsulates a DeviceArray `x`.
  
    Takes ownership of the contents of `x`; leaves `x` in an invalid/deleted
    state.
  
    Args:
      x: a `DeviceArray`, on either CPU or GPU.
    """
    if not isinstance(x, xla.DeviceArray):
      raise TypeError("Argument to to_dlpack must be a DeviceArray, got {}"
                      .format(type(x)))
    buf = xla._force(x).device_buffer
    return xla_client._xla.BufferToDLPackManagedTensor(buf)
  
  def from_dlpack(dlpack, backend=None):
    """Returns a `DeviceArray` representation of a DLPack tensor `dlpack`.
  
    The returned `DeviceArray` shares memory with `dlpack`.
  
    Args:
      dlpack: a DLPack tensor, on either CPU or GPU.
      backend: experimental, optional: the platform on which `dlpack` lives.
    """
    # TODO(phawkins): ideally the user wouldn't need to provide a backend and we
    # would be able to figure it out from the DLPack.
    backend = backend or xla_bridge.get_backend()
    client = getattr(backend, "client", backend)
    buf = xla_client._xla.DLPackManagedTensorToBuffer(dlpack, client)
    xla_shape = buf.shape()
    assert not xla_shape.is_tuple()
    aval = core.ShapedArray(xla_shape.dimensions(), xla_shape.numpy_dtype())
    return xla.DeviceArray(aval, buf.device(), lazy.array(aval.shape), buf)  # pytype: disable=attribute-error
  
subject: Make dlpack code robust against upcoming XLA Python binding change. (#2876)