// Fills data with values [start,end) with given step size.
void Range(vector<int32_t>* data, int32_t start, int32_t end,
           int32_t step = 1) {
  for (int32_t i = start; i < end; i += step) {
    (*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++) {

pkg syscall (freebsd-386), type Stat_t struct, Blksize uint32
pkg syscall (freebsd-386), type Stat_t struct, Dev uint32
pkg syscall (freebsd-386), type Stat_t struct, Gen uint32
pkg syscall (freebsd-386), type Stat_t struct, Ino uint32
pkg syscall (freebsd-386), type Stat_t struct, Lspare int32
pkg syscall (freebsd-386), type Stat_t struct, Nlink uint16
pkg syscall (freebsd-386), type Stat_t struct, Pad_cgo_0 [8]uint8

  // Map from tensor ID to how many references still exist for this tensor in
  // the tape.
  std::unordered_map<int64_t, int64_t> tensor_usage_counts;

  // Maps from op ID to how many output tensors of this op still need to have
  // their gradients computed.
  std::unordered_map<int64_t, int64_t> op_missing_tensor;
};

// If `persistent_tape` is true, op_tape is not changed and none of the

  TapeVSpace vspace(ctx);
  std::vector<int64_t> target_tensor_ids = MakeTensorIDList(targets);
  std::vector<int64_t> source_tensor_ids = MakeTensorIDList(sources);
  tensorflow::gtl::FlatSet<int64_t> sources_set(source_tensor_ids.begin(),
                                                source_tensor_ids.end());
  std::unordered_map<int64_t, TapeTensor> sources_that_are_targets;

-     __extension__ __STRING2_SMALL_GET32 (src, 4)
-#  endif
-__STRING_INLINE char *__strcpy_small (char *, __uint16_t, __uint16_t,
-				      __uint32_t, __uint32_t, size_t);
 __STRING_INLINE char *
 __strcpy_small (char *__dest,
 		__uint16_t __src0_2, __uint16_t __src4_2,
@@ -482,42 +257,6 @@ __strcpy_small (char *__dest,
   return __dest;
 }
 # else
-#  ifndef _FORCE_INLINES

void TestHandleFromDLPack(TF_Status* status, TFE_Context* ctx,
                          std::vector<int64_t> shape,
                          std::vector<int64_t> strides) {
  size_t num_elements = 1;
  for (int i = 0; i < static_cast<int32_t>(shape.size()); ++i) {
    num_elements *= shape[i];
  }
  std::vector<float> data(num_elements);
  for (size_t j = 0; j < num_elements; ++j) {
    data[j] = j;
  }
  DLManagedTensor dlm_in = {};

  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }

    size_t requested_read_length;
    if (n > INT32_MAX)
      requested_read_length = INT32_MAX;
    else
      requested_read_length = n;

    // `pread` returns a `ssize_t` on POSIX, but due to interface being
    // cross-platform, return type of `Read` is `int64_t`.
    int64_t r = int64_t{pread(posix_file->fd, dst, requested_read_length,
                              static_cast<off_t>(offset))};
    if (r > 0) {

    stat_cache_max_entries = static_cast<size_t>(value);
  }
  stat_cache = std::make_unique<ExpiringLRUCache<GcsFileStat>>(
      stat_cache_max_age, stat_cache_max_entries);
}

GCSFile::GCSFile(google::cloud::storage::Client&& gcs_client, bool compose,
                 uint64_t block_size, size_t max_bytes, uint64_t max_staleness,
                 uint64_t stat_cache_max_age, size_t stat_cache_max_entries)

  int64_t X_dims[] = {3, 3};
  AbstractTensorHandlePtr X;
  {
    AbstractTensorHandle* X_raw;
    status_ = TestTensorHandleWithDims<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), X_vals, X_dims, 2, &X_raw);
    ASSERT_EQ(errors::OK, status_.code()) << status_.message();
    X.reset(X_raw);
  }
  // Label
  int32_t Y_vals[] = {1, 0, 1};
  int64_t Y_dims[] = {3};