std::unique_ptr<RandomAccessFile>* result) {
  if (ops_->new_random_access_file == nullptr)
    return errors::Unimplemented(tensorflow::strings::StrCat(
        "Filesystem for ", fname, " does not support NewRandomAccessFile()"));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  auto file = std::make_unique<TF_RandomAccessFile>();
  std::string translated_name = TranslateName(fname);

# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick
# check This script checks for such errors early
if ! grep "rbe_$TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX" .bazelrc; then
  cat <<EOF
ERROR: RBE was enabled via the 'rbe' env in the 'TFCI' variable.
       TFCI: $TFCI

  virtual bool PreferCustomSummarizer() const { return false; }

  // Returns a string which summarizes the value of this TensorHandle, for
  // debugging. Does not include a shape or dtype.
  //
  // Included in the default implementation of DebugString.
  virtual Status SummarizeValue(std::string& summary) const;

  // For LLVM style RTTI.

      ReluModel, ReluGradModel, immediate_execution_ctx_.get(), {X.get()},
      UseFunction()));

  // Mathematically, Relu isn't differentiable at `0`. So `gradient_checker`
  // does not work with it.
  AbstractTensorHandlePtr Y;
  {
    AbstractTensorHandle* Y_raw;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 0.0f, &Y_raw);

    if (offset >= pos + data.size()) {
      // The requested offset is at or beyond the end of the file. This can
      // happen if `offset` is not block-aligned, and the read returns the last
      // block in the file, which does not extend all the way out to `offset`.
      std::stringstream os;
      os << "EOF at offset " << offset << " in file " << filename
         << " at position " << pos << " with data size " << data.size();

  // fetcher calls that the cache makes.
  calls.push_back(0);
  // Cache miss - drains an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  // Cache hit - does not drain an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  calls.push_back(block_size);
  // Cache miss followed by cache hit.
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));

  TF_AbstractFunction* func =
      TF_FinalizeFunction(graph_ctx, add_outputs, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  // Note: TF_OutputList does not own the underlying AbstractTensors, those
  // need to be deleted explicitly.
  TF_DeleteAbstractTensor(TF_OutputListGet(add_outputs, 0));

  // Build eager context.

Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);

namespace {

static void ExpectHasSubstr(StringPiece s, StringPiece expected) {
  EXPECT_TRUE(absl::StrContains(s, expected))
      << "'" << s << "' does not contain '" << expected << "'";
}

// Returns the GPU device name if there is one (with arbitrary tie breaking if
// there are more than one), or "" otherwise.
string GPUDeviceName(TF_Session* session) {

# Delete modules that should be hidden from dir().
# Don't fail if these modules are not available.
# For e.g. this file will be originally placed under tensorflow/_api/v1 which
# does not have "python", "core" directories. Then, it will be copied
# to tensorflow/ which does have these two directories.
try:
  del python
except NameError:
  pass
try:
  del core
except NameError:
  pass
try:
  del compiler

  // When creating file, use the same permissions as original
  mode_t open_mode = mode & (S_IRWXU | S_IRWXG | S_IRWXO);

  // O_WRONLY | O_CREAT | O_TRUNC:
  //   Open file for write and if file does not exist, create the file.
  //   If file exists, truncate its size to 0.
  int dst_fd = open(dst, O_WRONLY | O_CREAT | O_TRUNC, open_mode);
  if (dst_fd < 0) {
    close(src_fd);
    return -1;
  }