}
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,
              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {
    DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,
            expect_failure);
  }

const char* TF_OperationName(TF_Operation* oper) {
  return oper->node.name().c_str();
}

const char* TF_OperationOpType(TF_Operation* oper) {
  return oper->node.type_string().c_str();
}

const char* TF_OperationDevice(TF_Operation* oper) {
  return oper->node.requested_device().c_str();
}

int TF_OperationNumOutputs(TF_Operation* oper) {
  return oper->node.num_outputs();
}

  bool found_neg = false;
  size_t pos = 0;
  TF_Operation* oper;
  while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) {
    if (oper == feed) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (oper == three) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (oper == add) {
      EXPECT_FALSE(found_add);
      found_add = true;

    jvmArgumentProviders.add(CommandLineArgumentProvider {
        //allow ProjectBuilder to inject legacy types into the system classloader
        if (testVersionProvider.get().canCompileOrRun(9)) {
            listOf("--add-opens", "java.base/java.lang=ALL-UNNAMED")
        } else {
            emptyList()
        }
    })
    jvmArgumentProviders.add(CommandLineArgumentProvider {
        val testVersion = testVersionProvider.get()

		return nil, errExpectedBool
	}
	return FromBool(!b), nil
}

func (e *ConditionOperand) evalNode(r Record, tableAlias string) (*Value, error) {
	opVal, opErr := e.Operand.evalNode(r, tableAlias)
	if opErr != nil || e.ConditionRHS == nil {
		return opVal, opErr
	}

	// Need to evaluate the ConditionRHS
	switch {
	case e.ConditionRHS.Compare != nil:

  TF_DeleteBuffer(buffer);
  TF_DeleteStatus(s);
  return ret;
}

bool GetNodeDef(TF_Operation* oper, tensorflow::NodeDef* node_def) {
  TF_Status* s = TF_NewStatus();
  TF_Buffer* buffer = TF_NewBuffer();
  TF_OperationToNodeDef(oper, buffer, s);
  bool ret = TF_GetCode(s) == TF_OK;
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

### Cache the OpenAPI schema { #cache-the-openapi-schema }

You can use the property `.openapi_schema` as a "cache", to store your generated schema.

That way, your application won't have to generate the schema every time a user opens your API docs.

It will be generated only once, and then the same cached schema will be used for the next requests.

{* ../../docs_src/extending_openapi/tutorial001_py39.py hl[13:14,25:26] *}