TFE_MonitoringCounter(const char* name, const char* description,
                        LabelDesc&&... label) {
    counter = absl::WrapUnique(tensorflow::monitoring::Counter<NumLabels>::New(
        name, description, label...));
  }

  std::unique_ptr<tensorflow::monitoring::Counter<NumLabels>> counter;
};

struct TFE_MonitoringCounter0 : TFE_MonitoringCounter<0> {
  using TFE_MonitoringCounter::TFE_MonitoringCounter;
};

  return ok;
}

TEST(CAPI, MonitoringCounter0) {
  TF_Status* status = TF_NewStatus();
  auto* counter =
      TFE_MonitoringNewCounter0("test/counter", status, "description");
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
  auto* cell = TFE_MonitoringGetCellCounter0(counter);
  TFE_MonitoringCounterCellIncrementBy(cell, 1);
  EXPECT_EQ(TFE_MonitoringCounterCellValue(cell), 1);

  const int callers = 4;
  BlockingCounter counter(callers);
  auto fetcher = [&counter](const string& filename, size_t offset, size_t n,
                            char* buffer, TF_Status* status) -> int64_t {
    counter.DecrementCount();
    if (!counter.WaitFor(std::chrono::seconds(10))) {
      // This avoids having the test time out, which is harder to debug.

# through bazel. This must be done in a different root directory, //bazel_pip/...,
# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 
# Pass --config=pip to run the same suite of tests. If you want to run just one
# test for investigation, you'll need --config=pip_venv instead, and then you

  // Validate GraphDef is what we expect.
  bool found_placeholder = false;
  bool found_scalar_const = false;
  bool found_add = false;
  for (const auto& n : graph_def.node()) {
    if (IsPlaceholder(n)) {
      EXPECT_FALSE(found_placeholder);
      found_placeholder = true;
    } else if (IsScalarConst(n, 3)) {
      EXPECT_FALSE(found_scalar_const);
      found_scalar_const = true;
    } else if (IsAddN(n, 2)) {

      ["grep", "-rlE", '(failures|errors)="[1-9]', sys.argv[1]]
  )
except subprocess.CalledProcessError as e:
  print("No failures found to log!")
  exit(0)

# For test cases, only show the ones that failed that have text (a log)
seen = collections.Counter()
runfiles_matcher = re.compile(r"(/.*\.runfiles/)")


for f in files.strip().splitlines():
  # Just ignore any failures, they're probably not important

//
// The code under test will have created streamz counters like this:
// auto* streamz = tensorflow::monitoring::Counter<1>::New("name",
// "description", "label");
// and then incremented that counter for various values of label:
// streamz->GetCell("label-value")->IncrementBy(1);
//
// The test code can then read and test the value of that counter:
//

The CI folder contains the configuration files and scripts used to build, test,
and deploy TensorFlow. This folder is typically used by continuous integration
(CI) tools to build and test TensorFlow whenever there is a change to the
code. This folder is broken into subfolders that represent the level of support

def print_invocation_results(result_store_dict: ResultDictType):
  """Prints out a short summary of the found ResultStore links (if any)."""
  print()
  if not result_store_dict:
    print('Found no ResultStore links for Bazel build/test invocations.')
  else:
    print(f'Found {len(result_store_dict)} ResultStore link(s) for '
          f'Bazel invocations.\n'

 * @license
 * Copyright 2021 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,