&arrived, &executed, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Create a variable handle placed on the custom device.
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  /// 3) `TF_OUT_OF_RANGE` if the read from the remote filesystem succeeded, but
  ///    the file contents do not extend past `offset` and thus nothing was
  ///    placed in `out`.
  /// 4) `TF_OK` otherwise (i.e. the read succeeded, and at least one byte was
  /// placed
  ///    in `buffer`).
  ///
  /// Caller is responsible for allocating memory for `buffer`.
  /// `buffer` will be left unchanged in case of errors.

    }
    lru_list_.erase(it->second.lru_iterator);
    cache_.erase(it);
    return true;
  }

  /// The maximum age of entries in the cache, in seconds. A value of 0 means
  /// that no entry is ever placed in the cache.
  const uint64_t max_age_;

  /// The maximum number of entries in the cache. A value of 0 means there is no
  /// limit on entry count.
  const size_t max_entries_;

      _os.getenv("TF_PLUGGABLE_DEVICE_LIBRARY_PATH")
  )

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.

try:
  del python
except NameError:
  pass
try:

  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will
  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,

  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;
  // Returns the device where the tensor was placed.
  virtual const char* BackingDeviceName(Status* status) const = 0;
  // Returns the device type which created the handle.
  virtual const char* DeviceType(Status* status) const = 0;

*   DeviceFactory's AddDevices and CreateDevices functions now return a Status
    instead of void.
*   Int32 elements of list(type) arguments are no longer placed in host memory
    by default. If necessary, a list(type) argument to a kernel can be placed in
    host memory using a HostMemory annotation.
*   `uniform_unit_scaling_initializer()` no longer takes a `full_shape` arg,

      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

// Create a new TFE_TensorHandle with the same contents as 'h' but placed
// in the memory of the device name 'device_name'.
// If source and destination are the same device, then this creates a new handle
// that shares the underlying buffer. Otherwise, it currently requires at least
// one of the source or destination devices to be CPU (i.e., for the source or
// destination tensor to be placed in host memory).

except (ImportError, AttributeError):
  pass

del importlib

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.
try:
  del python
except NameError:
  pass
try: