print(error_msg % val)
    environ_cp[var_name] = ''
  else:
    raise UserInputError('Invalid %s setting was provided %d times in a row. '
                         'Assuming to be a scripting mistake.' %
                         (var_name, n_ask_attempts))

  if resolve_symlinks:
    val = os.path.realpath(val)
  environ_cp[var_name] = val
  return val

      ParallelTensor::FromTensorHandles(parallel_device,
                                        std::move(mixed_handles), status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK);
  // Can't take the shape of a parallel tensor with varying numbers of axes, but
  // running operations on them is OK.
  TF_ASSERT_OK(unknown_length_vector->Shape(&shape));
  EXPECT_THAT(*shape, ElementsAre(-1));

  std::vector<Tensor> all_input_tensors;
  // Update the vector with information from `input_tensors` if provided.
  if (input_tensors != nullptr) {
    // Note that we take the address of the elements in `all_input_tensors`
    // below. Allocate enough space so that no reallocation happens, which will
    // make the pointers invalid.
    all_input_tensors.reserve(num_inputs);

// Tensor passed to Accumulate goes out of scope.
//
// Not thread-safe.
template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class ForwardAccumulator {
 public:
  // Does not take ownership of `vspace`, which must outlive the
  // ForwardAccumulator.
  explicit ForwardAccumulator(
      const VSpace<Gradient, BackwardFunction, TapeTensor>& vspace,
      bool use_batch)

build:rbe_cross_compile_macos_x86 --bes_upload_mode=nowait_for_upload_complete
test:rbe_cross_compile_macos_x86 --config=rbe_cross_compile_base
# Increase the test timeout as tests often take longer on mac.
test:rbe_cross_compile_macos_x86 --test_timeout=300,450,1200,3600
# Limit jobs to 100 to avoid running into "out of memory" issues (b/316266643)
build:rbe_cross_compile_macos_x86 --jobs=100

![image](https://github.com/tensorflow/tensorflow/assets/52792999/3eea4ca5-daa0-4570-b0b5-2a2b03a724a3)

### Contributor License Agreements

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

// ResourceMgr to clean up the resource. `status` will be set. If `status` is
// not OK, `*result_plugin_resource` will be set as nullptr.
//
// Caller does not take ownership of the `plugin_resource`.
TF_CAPI_EXPORT extern void TF_LookupOrCreatePluginResource(
    TF_OpKernelContext* ctx, const char* container_name,
    const char* plugin_resource_name, void** result_plugin_resource,

  // List attributes of available devices
  virtual void ListDevices(std::vector<DeviceAttributes>* devices) = 0;

  // Add `devices` into context's device manager. Context's device manager
  // will take ownership and maintain devices' lifetime.
  virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0;

  // Block until all pending nodes are finished.
  virtual Status AsyncWait() = 0;

  ///
  /// A new `char*` buffer must be allocated by this method. Core TensorFlow
  /// manages the lifetime of the buffer after the call. Thus, all callers of
  /// this method must take ownership of the returned pointer.
  ///
  /// The implementation should clean up paths, including but not limited to,
  /// removing duplicate `/`s, and resolving `..` and `.`.
  ///

    *   Promoting `tf.data.experimental.snapshot` API to
        `tf.data.Dataset.shapshot` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.take_while` API to
        `tf.data.Dataset.take_while` and deprecating the experimental endpoint.
    *   Promoting `tf.data.experimental.ThreadingOptions` API to
        `tf.data.ThreadingOptions` and deprecating the experimental endpoint.