// destructor. The easiest way to use MakeCleanup is with a lambda argument,
// capturing the return value in an 'auto' local variable. Most users will not
// need more sophisticated syntax than that.
//
// Example:
//   void func() {
//     FILE* fp = fopen("data.txt", "r");
//     if (fp == nullptr) return;
//     auto fp_cleaner = gtl::MakeCleanup([fp] { fclose(fp); });

# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets
# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

  # grep patterns for targets which are allowed to be missing from the licenses

// tensors in `retvals`.
//
// 'retvals' must point to a pre-allocated array of TFE_TensorHandle* and
// '*num_retvals' should be set to the size of this array. It is an error if
// the size of 'retvals' is less than the number of outputs. This call sets
// *num_retvals to the number of outputs.
//
// If async execution is enabled, the call may simply enqueue the execution

                                     " but actual rank is ", rank);
    return;
  }

  if (num_dims == 0) {
    // Output shape is a scalar.
    return;
  }

  // Rank is greater than 0, so fill in the values, if known, and
  // -1 for unknown values.
  for (int i = 0; i < num_dims; ++i) {
    auto dim = ic->Dim(shape, i);
    int64_t value = -1;
    if (ic->ValueKnown(dim)) {

    EXPECT_EQ(got_n, want_n);
    calls++;
    memset(buffer, 'x', got_n);
    TF_SetStatus(status, TF_OK, "");
    return got_n;
  };
  // If block_size, max_bytes, or both are zero, or want_n is larger than
  // max_bytes the cache is a pass-through.
  tf_gcs_filesystem::RamFileBlockCache cache1(1, 0, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache2(0, 1, 0, fetcher);

  if (!status.ok())
    GTEST_SKIP() << "NewRandomAccessFile() not supported: " << status;

  char scratch[64 /* must be bigger than test_data */] = {0};
  StringPiece result;
  // read at least 1 byte more than test_data
  status = read_file->Read(0, test_data.size() + 1, &result, scratch);
  EXPECT_PRED2(UnimplementedOrReturnsCode, status, Code::OUT_OF_RANGE);
}

  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) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Status* s = status.get();

# directly under the root to the "maven-style" src/main/java layout
#
# Source files generated by annotation processors appear directly
# under the root of srcjars jars created by bazel, rather than under
# the maven-style src/main/java subdirectory.
#
# Bazel manages annotation generated source as follows: First, it
# calls javac with options that create generated files under a

  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  char* dst = buffer;
  int64_t read = 0;

  while (n > 0) {
    // Some platforms, notably macs, throw `EINVAL` if `pread` is asked to read
    // more than fits in a 32-bit integer.
    size_t requested_read_length;
    if (n > INT32_MAX)
      requested_read_length = INT32_MAX;
    else
      requested_read_length = n;

# the dependencies of that target which include a license notice file are then
# included when generating that license. Necessary because the license targets
# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

  # grep patterns for targets which are allowed to be missing from the licenses