memset(buffer, 'x', n);
    TF_SetStatus(status, TF_OK, "");
    return n;
  };
  const uint32 block_count = 256;
  tf_gcs_filesystem::RamFileBlockCache cache(
      block_size, block_count * block_size, 0, fetcher);
  std::vector<char> out;
  out.resize(block_count, 0);
  // The cache has space for `block_count` blocks. The loop with i = 0 should
  // fill the cache, and the loop with i = 1 should be all cache hits. The

      new TensorSliceReader::VarToDataTypeMap);
  v2_reader_->Seek(kHeaderEntryKey);
  for (v2_reader_->Next(); v2_reader_->Valid(); v2_reader_->Next()) {
    if (filtered_keys.count(string(v2_reader_->key())) > 0) continue;
    CHECK(entry.ParseFromArray(v2_reader_->value().data(),
                               v2_reader_->value().size()))
        << entry.InitializationErrorString();

void TF_DeleteRecursively(const char* dirname, int64_t* undeleted_file_count,
                          int64_t* undeleted_dir_count, TF_Status* status) {
  ::int64_t f, d;

  TF_SetStatus(status, TF_OK, "");
  ::tensorflow::Set_TF_Status_from_Status(
      status, ::tensorflow::Env::Default()->DeleteRecursively(dirname, &f, &d));
  *undeleted_file_count = f;
  *undeleted_dir_count = d;
}

    if (edge->src_output() == oper_out.index) {
      if (count < max_consumers) {
        consumers[count] = {ToOperation(edge->dst()), edge->dst_input()};
      }
      ++count;
    }
  }
  return count;
}

int TF_OperationNumControlInputs(TF_Operation* oper) {
  int count = 0;
  for (const auto* edge : oper->node.in_edges()) {
    if (edge->IsControlEdge() && !edge->src()->IsSource()) {

          }
        }
      }
      auto usage_count_it = state.tensor_usage_counts.find(id);
      if (usage_count_it == state.tensor_usage_counts.end()) {
        VLOG(1) << "Tensor " << id << " not used";
        continue;
      }
      usage_count_it->second--;
      if (usage_count_it->second > 0) {
        VLOG(1) << "Tensor " << id << " usage count " << usage_count_it->second;
        continue;
      }

# limitations under the License.
# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then

# limitations under the License.
# ==============================================================================
source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then

# proto-message: ActionSpecs

# Python rules should not have more than one source file.
action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }

                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}

void TFE_MonitoringDeleteBuckets(TFE_MonitoringBuckets* buckets) {
  delete buckets;

                    verbose: bool = False):
  """Creates a JUnit-based XML file, with each invocation as a testcase."""
  os.makedirs(os.path.dirname(output_path), exist_ok=True)
  failure_count = 0
  error_count = 0

  date_time = datetime.datetime
  attrib = {'name': 'Bazel Invocations', 'time': '0.0',
            'timestamp': date_time.isoformat(date_time.utcnow())}
  testsuites = ElemTree.Element('testsuites')