## Wheel Test

This directory is dedicated to tests that require a built TensorFlow wheel
file for testing, such as:

* Ensuring the entire API is importable
* Testing downstream projects against the wheel

Ensure you have Bazel installed and accessible from your command line.

These tests use hermetic Python. They also require a built TensorFlow wheel file
and a requirements_lock file. The requirements_lock file is generated by the

# if there is a change in requirements.in then all lock files will be updated
# accordingly.

# -e: abort script if one command fails
# -u: error if undefined variable used
# -o pipefail: entire command fails if pipe fails. watch out for yes | ...
# -o history: record shell history
set -euo pipefail -o history

# Check for required arguments
if [ -z "$1" ]; then

implement, and therefore security issues deriving from their absence are not
considered a vulnerability in TensorFlow.

### Resource allocation

A denial of service caused by one model could bring down the entire server, but
we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from

    - name: Update the RBE Configs
      run: |
        function map() {
          # The "digest" that allows us to pull an image is not the digest as
          # returned by the API, but a sha256sum of the entire chunk of image
          # metadata. gcr.io helpfully includes it in the header of the response
          # as docker-content-digest: sha256:[digest]. Note we use egrep to

    to fix this.
-   Once the internal tests pass, we go ahead and merge the code internally as
    well as externally on GitHub.

In a graphical form, the entire lifetime of a PR looks like

![image](https://github.com/tensorflow/tensorflow/assets/52792999/3eea4ca5-daa0-4570-b0b5-2a2b03a724a3)

### Contributor License Agreements

Recursively find all the JUnit test.xml files in one directory, and merge any
of them that contain failures into one file. The TensorFlow DevInfra team
uses this to generate a simple overview of an entire pip and nonpip test
invocation, since the normal logs that Bazel creates are too large for the
internal invocation viewer.
"""
import collections
import os
import re
import subprocess
import sys

# simple" is MUCH more important than "don't repeat yourself."

# -e: abort script if one command fails
# -u: error if undefined variable used
# -x: log all commands
# -o pipefail: entire command fails if pipe fails. watch out for yes | ...
# -o history: record shell history
# -o allexport: export all functions and variables to be available to subscripts
#               (affects 'source $TFCI')

  for (int i = 0; i < num_entries; i++)
    (*entries)[i] = strdup(childrens[i].c_str());
  TF_SetStatus(status, TF_OK, "");
  return num_entries;
}

void Stat(const TF_Filesystem* filesystem, const char* path,
          TF_FileStatistics* stats, TF_Status* status) {
  std::string bucket, object;
  ParseGCSPath(path, true, &bucket, &object, status);
  if (TF_GetCode(status) != TF_OK) return;

  // timestamp of the entry.
  cache.Insert(key, 41);
  env->SetNowSeconds(2);
  cache.Insert(key, 42);
  // 1 second after the most recent insertion, the entry is still valid.
  env->SetNowSeconds(3);
  int value = 0;
  EXPECT_TRUE(cache.Lookup(key, &value));
  EXPECT_EQ(value, 42);
  // 2 seconds after the most recent insertion, the entry is no longer valid.
  env->SetNowSeconds(4);

namespace tensorflow {
namespace {

// The tests are parameterized on:
// - a string representing the tracing implementation: "mlir" or "graphdef".
// - a boolean that when true enables TFRT as the execution engine.
class UnifiedCAPI
    : public ::testing::TestWithParam<std::tuple<const char*, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());