}

TF_Tensor* BoolTensor(bool v) {
  const int num_bytes = sizeof(bool);
  bool* values = new bool[1];
  values[0] = v;
  return TF_NewTensor(TF_BOOL, nullptr, 0, values, num_bytes, &BoolDeallocator,
                      nullptr);
}

TF_Tensor* Int8Tensor(const int64_t* dims, int num_dims, const char* values) {
  int64_t num_values = 1;
  for (int i = 0; i < num_dims; ++i) {
    num_values *= dims[i];
  }
  TF_Tensor* t =

  @AndroidIncompatible // OutOfMemoryError
  public void testLargeBloomFilterDoesntOverflow() {
    long numBits = Integer.MAX_VALUE;
    numBits++;

    LockFreeBitArray bitArray = new LockFreeBitArray(numBits);
    assertTrue(
        "BitArray.bitSize() must return a positive number, but was " + bitArray.bitSize(),
        bitArray.bitSize() > 0);

TEST(CAPI, TestTensorNonScalarBytesAllocateDelete) {
  const int batch_size = 4;
  const int num_dims = 2;
  int64_t* dims = new int64_t[num_dims];
  int64_t num_elements = 1;
  dims[0] = batch_size;
  dims[1] = 1;
  for (int64_t i = 0; i < num_dims; ++i) {
    num_elements *= dims[i];
  }
  TF_Tensor* t = TF_AllocateTensor(TF_STRING, dims, num_dims,
                                   sizeof(TF_TString) * num_elements);

    BigInteger result = randomPositiveBigInteger(numBits);
    return RANDOM_SOURCE.nextBoolean() ? result : result.negate();
  }

  /**
   * Chooses a number in (-2^numBits, 2^numBits) at random, with density concentrated in numbers of
   * lower magnitude.
   */
  static BigInteger randomBigInteger(int numBits) {
    while (true) {
      if (RANDOM_SOURCE.nextBoolean()) {

    BigInteger result = randomPositiveBigInteger(numBits);
    return RANDOM_SOURCE.nextBoolean() ? result : result.negate();
  }

  /**
   * Chooses a number in (-2^numBits, 2^numBits) at random, with density concentrated in numbers of
   * lower magnitude.
   */
  static BigInteger randomBigInteger(int numBits) {
    while (true) {
      if (RANDOM_SOURCE.nextBoolean()) {

    DCHECK(shape != nullptr);
    TF_Status status;
    int num_dims = TF_GraphGetTensorNumDims(graph_, output_, &status);
    DCHECK_GE(num_dims, -1);
    TF_RETURN_IF_ERROR(StatusFromTF_Status(&status));
    if (num_dims == kUnknownRank) {
      return absl::OkStatus();
    }

    std::vector<int64_t> dims(num_dims, kUnknownDim);
    TF_GraphGetTensorShape(graph_, output_,

		[case(POLICY_INFO_DNS_DOMAIN)] LsarDnsDomainInfo dns_domain;
	} LsarPolicyInfo;

	typedef struct {
		sid_t *sid;
	} LsarSidPtr;

	typedef struct {
		[range(0,1000)] uint32_t num_sids;
		[size_is(num_sids)] LsarSidPtr *sids;
	} LsarSidArray;

	typedef enum {
		SID_NAME_USE_NONE = 0, /* NOTUSED */
		SID_NAME_USER     = 1, /* user */
		SID_NAME_DOM_GRP  = 2, /* domain group */

		[case(POLICY_INFO_DNS_DOMAIN)] LsarDnsDomainInfo dns_domain;
	} LsarPolicyInfo;

	typedef struct {
		sid_t *sid;
	} LsarSidPtr;

	typedef struct {
		[range(0,1000)] uint32_t num_sids;
		[size_is(num_sids)] LsarSidPtr *sids;
	} LsarSidArray;

	typedef enum {
		SID_NAME_USE_NONE = 0, /* NOTUSED */
		SID_NAME_USER     = 1, /* user */
		SID_NAME_DOM_GRP  = 2, /* domain group */

                                 const int64_t* dims, int num_dims) {
  DCHECK(index >= 0 && index < shape_list->num_items);
  TF_ShapeAndType& shape = shape_list->items[index];
  DCHECK(shape.dims == nullptr) << "Shape at " << index << " is already set!";
  DCHECK(num_dims >= 0) << "Number of dimensions cannot be negative!";
  shape.num_dims = num_dims;
  shape.dims = new int64_t[num_dims];
  memcpy(shape.dims, dims, sizeof(int64_t) * num_dims);

    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.