}
Status TapeOperation::SetAttrBoolList(const char* attr_name,
                                      const unsigned char* values,
                                      int num_values) {
  std::unique_ptr<bool[]> b(new bool[num_values]);
  for (int i = 0; i < num_values; ++i) {
    b[i] = values[i];
  }
  forward_op_.attrs.Set(attr_name,
                        gtl::ArraySlice<const bool>(b.get(), num_values));

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

AbstractContext* BuildFunction(const char* fn_name) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_ExecutionContext* graph_ctx = TF_CreateFunction(fn_name, status.get());
  return unwrap(graph_ctx);
}

	if value.Kind() == reflect.Ptr && value.IsNil() {
		value = reflect.New(value.Type().Elem())
	}
	modelType := reflect.Indirect(value).Type()

	if modelType.Kind() == reflect.Interface {
		modelType = reflect.Indirect(reflect.ValueOf(dest)).Elem().Type()
	}

	for modelType.Kind() == reflect.Slice || modelType.Kind() == reflect.Array || modelType.Kind() == reflect.Ptr {
		modelType = modelType.Elem()
	}

// In separate directory to isolate #pragma GCC diagnostic.

package issue27340

// We use the #pragma to avoid a compiler warning about incompatible
// pointer types, because we generate code passing a struct ptr rather
// than using the typedef. This warning is expected and does not break
// a normal build.
// We can only disable -Wincompatible-pointer-types starting with GCC 5.

// #if __GNU_MAJOR__ >= 5
//

  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  EXPECT_TF_META("v", list.size(), TF_ATTR_STRING, list_total_size);
  std::unique_ptr<void*[]> values(new void*[list.size()]);
  std::unique_ptr<size_t[]> lens(new size_t[list.size()]);
  std::unique_ptr<char[]> storage(new char[list_total_size]);
  TF_OperationGetAttrStringList(oper, "v", values.get(), lens.get(),

  TF_SetStatus(status, TF_OK, "");
  const tensorflow::OpDef* op_def_ptr = nullptr;
  absl::Status s =
      reinterpret_cast<tensorflow::FunctionLibraryDefinition*>(fn_lib)
          ->LookUpOpDef(name, &op_def_ptr);
  if (!s.ok()) {
    tsl::Set_TF_Status_from_Status(status, s);
    return;
  }

  s = tensorflow::MessageToBuffer(*op_def_ptr, buf);
  if (!s.ok()) {
    tsl::Set_TF_Status_from_Status(status, s);

Status RegisterGradients(GradientRegistry* registry) {
  TF_RETURN_IF_ERROR(RegisterNotDifferentiable(registry, "CheckNumerics"));
  return absl::OkStatus();
}

TEST_P(CppGradients, TestSetAttrString) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  AbstractContextPtr ctx;
  {
    AbstractContext* ctx_raw = nullptr;
    Status s =

                                                             TFE_Context* ctx);

// Calls the destructor of DLManagedTensor, used in the destructor of PyCapsule.
TF_CAPI_EXPORT extern void TFE_CallDLManagedTensorDeleter(void* dlm_ptr);
}  // namespace tensorflow

#ifndef TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_
#define TENSORFLOW_C_EAGER_ABSTRACT_FUNCTION_H_

#include "absl/status/statusor.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/platform/intrusive_ptr.h"
#include "tensorflow/core/platform/refcount.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {

class FunctionRecord;

// register/use filesystems.

namespace tensorflow {

using UniquePtrTo_TF_Status =
    ::std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)>;

Status ModularFileSystem::NewRandomAccessFile(
    const std::string& fname, TransactionToken* token,
    std::unique_ptr<RandomAccessFile>* result) {
  if (ops_->new_random_access_file == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(