# only on affected targets, usually. There are targets in //tensorflow/lite that
# don't pass --nobuild, so they're on their own.
#
# Although buildifier checks for formatting as well, "bazel build nobuild"
# checks for cross-file issues like bad includes or missing BUILD definitions.
#
# We can't test on the windows toolchains because they're using a legacy
# toolchain format (or something) that specifies the toolchain directly instead

_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise
_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
_current_module = _sys.modules[__name__]

// Core APIs
// -----------------------------------------------------------------------------

// A TF_ExecutionContext stores knowledge about how to execute an operation.
// E.g. it could know whether we're in eager mode or graph mode, keeps track
// of gradient tapes, etc.
typedef struct TF_ExecutionContext TF_ExecutionContext;

// A TF_AbstractTensor is an input to an operation. E.g. it could be a union

# only on affected targets, usually. There are targets in //tensorflow/lite that
# don't pass --nobuild, so they're on their own.
#
# Although buildifier checks for formatting as well, "bazel build nobuild"
# checks for cross-file issues like bad includes or missing BUILD definitions.
#
# We can't test on the windows toolchains because they're using a legacy
# toolchain format (or something) that specifies the toolchain directly instead

   "${TARGET}/usr/lib64"


# Link in architecture specific includes from the system; note that we cannot
# link in the whole aarch64-linux-gnu folder, as otherwise we're overlaying
# system gcc paths that we do not want to find.
# TODO(klimek): Automate linking in all non-gcc / non-kernel include
# directories.
mkdir -p "${TARGET}/usr/include/aarch64-linux-gnu"

  """

  # First check to see if we're using a blessed version of the NDK.
  properties_path = '%s/source.properties' % android_ndk_home_path
  if is_windows() or is_cygwin():
    properties_path = cygpath(properties_path)
  with open(properties_path, 'r') as f:
    filedata = f.read()

  revision = re.search(r'Pkg.Revision = (\d+)', filedata)
  if revision:

  Otherwise your code may be broken by the change.

  """)

# Make sure directory containing top level submodules is in
# the __path__ so that "from tensorflow.foo import bar" works.
# We're using bitwise, but there's nothing special about that.
_API_MODULE = _sys.modules[__name__].bitwise  # pylint: disable=undefined-variable
_current_module = _sys.modules[__name__]

  tensors.reserve(output_tensors.size());
  for (const TapeTensor& o : output_tensors) {
    // Note: the tensor can have already been watched and hence be in the tape,
    // so we cannot check that we're inserting it here.
    tensor_tape_[o.GetID()] = op_id;
    tensor_usage_[o.GetID()] = 1;
    tensors.push_back(o);
  }
  op_tape_[op_id] = OpTapeEntry<BackwardFunction, TapeTensor>{

    TFE_Executor*, TF_Status* status);

// When an error happens, any pending operations are discarded, and newly issued
// ops return an error. This call clears the error state and re-enables
// execution of newly issued ops.
//
// Note that outputs of discarded ops remain in a corrupt state and should not
// be used for future calls.

    tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(