}

  /**
   * Explicitly specifies the return type of this {@code Invokable}. For example:
   *
   * <pre>{@code
   * Method factoryMethod = Person.class.getMethod("create");
   * Invokable<?, Person> factory = Invokable.of(getNameMethod).returning(Person.class);
   * }</pre>
   */
  public final <R1 extends R> Invokable<T, R1> returning(Class<R1> returnType) {
    return returning(TypeToken.of(returnType));
  }

    status->status = cc_status;
    return nullptr;
  }

  tensorflow::Tensor* tensor = var_info->var_info.var()->tensor();
  TF_Tensor* result_tensor =
      tensorflow::TF_TensorFromTensor(*tensor, &cc_status);
  status->status = cc_status;
  return result_tensor;
}

void TF_DeleteVariableInfo(TF_VariableInfo* var_info) {
  if (var_info != nullptr) {
    delete var_info;

  ParallelTensor* parallel_tensor = reinterpret_cast<ParallelTensor*>(
      TFE_TensorHandleDevicePointer(tensor, status));
  if (TF_GetCode(status) != TF_OK) return nullptr;
  if (parallel_tensor->num_tensors() == 1) {
    // Copy-off for single-device tensors is allowed to make debugging dynamic
    // control flow easier.
    return TFE_TensorHandleCopySharingTensor(parallel_tensor->tensor(0),

The MIT License (MIT)

Copyright (c) 2014 Sam Ghods

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

void GradientTape<Gradient, BackwardFunction, TapeTensor>::DeleteTrace(
    int64_t tensor_id) {
  auto it = tensor_usage_.find(tensor_id);
  if (it == tensor_usage_.end()) {
    return;
  }
  it->second--;
  if (it->second != 0) {
    return;
  }
  tensor_usage_.erase(it);
  auto tensor_op_it = tensor_tape_.find(tensor_id);
  if (tensor_op_it == tensor_tape_.end()) {
    return;
  }

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

					Reason:   "Namespace label istio-injection=enabled matches",
				},
				{
					Name:     "istio-sidecar-injector-1-16",
					Revision: "1-16",
					Reason:   "No matching namespace labels (istio.io/rev=1-16) or pod labels (istio.io/rev=1-16)",
				},
				{
					Name:     "istio-sidecar-injector-deactivated",
					Revision: "default",
					Reason:   "The injection webhook is deactivated, and will never match labels.",
				},

  if (VLOG_IS_ON(1) && status->status.ok()) {
    tensorflow::Tensor tensor;
    if (tensorflow::TF_TensorToTensor(ret, &tensor).ok()) {
      VLOG(1) << "Dequeued tensor content: " << tensor.DebugString();
    }
  }
  return ret;
}

void TF_EnqueueNamedTensor(TF_Session* session, int tensor_id,
                           TF_Tensor* tensor, TF_Status* status) {
  assert(session);
  {

  DLManagedTensor* dlm_tensor = &tf_dlm_tensor_ctx->tensor;
  dlm_tensor->manager_ctx = tf_dlm_tensor_ctx;
  dlm_tensor->deleter = &DLManagedTensorDeleter;
  dlm_tensor->dl_tensor.device = tf_dlm_context;
  int ndim = tensor->dims();
  dlm_tensor->dl_tensor.ndim = ndim;
  dlm_tensor->dl_tensor.data = tf_dlm_data;
  dlm_tensor->dl_tensor.dtype = tf_dlm_type;