#   export TFCI=...
#   export TF_BISECT_SCRIPT=ci/official/any.sh
#   export TF_BISECT_GOOD=a_good_commit_sha
#   export TF_BISECT_BAD=a_failing_commit_sha
#   export TF_ANY_TARGETS="quoted list of targets, like on the command line"
#   export TF_ANY_MODE=test
set -euxo pipefail
cd "$(dirname "$0")/../../"  # tensorflow/
export TFCI="$(echo $TFCI | sed 's/,nightly_upload/,public_cache,disk_cache/')"
git bisect start "$TF_BISECT_BAD" "$TF_BISECT_GOOD"

# nightly job. update_version.py affects TF_VER_SUFFIX, TF_VER_PYTHON, and
# TF_VER_FULL.

# Note: in awk, the command '/search/ {commands}' applies the commands to any line that
# matches the /search/ regular expression. "print $N" prints the Nth "field",
# where fields are strings separated by whitespace.
export TF_VER_MAJOR=$(awk '/#define TF_MAJOR_VERSION/ {print $3}' tensorflow/core/public/version.h)

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

    ASSERT_EQ(src.NumElements(), output.NumElements()) << line;
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      ASSERT_EQ(data[i], output.flat<tstring>()(i)) << line;
    }

    TF_DeleteTensor(dst);
  }
}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});
  TestEncodeDecode(__LINE__, {"hello"});
  TestEncodeDecode(__LINE__,

import distutils as _distutils
import importlib
import inspect as _inspect
import os as _os
import site as _site
import sys as _sys

# Do not remove this line; See https://github.com/tensorflow/tensorflow/issues/42596
from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow  # pylint: disable=unused-import
from tensorflow.python.tools import module_util as _module_util

IMPORTANT: For bazel invocations that uploaded to ResultStore (e.g. RBE), you
can view more detailed results that are probably easier to read than this log.
Try the links below:
EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates
  awk '/Streaming build results to/ {print $NF}' "$TFCI_OUTPUT_DIR/script.log" | uniq
}

# Print out any ResultStore URLs for Bazel invocations' results.

against a pending change. Search for "MLCI" internally to find it.

You may invoke a CI script of your choice by following these instructions:

```bash
cd tensorflow-git-dir

# Here is a single-line example of running a script on Linux to build the
# GPU version of TensorFlow for Python 3.12, using the public TF bazel cache and
# a local build cache:
TFCI=py312,linux_x86_cuda,public_cache,disk_cache ci/official/wheel.sh

Trevor Morris, Ubuntu, Vadim Borisov, vanderliang, wangsiyu, Wen Yun, Wen-Heng
(Jack) Chung, wenxizhu, William D. Irons, Xiaoming (Jason) Cui, Yan Facai (颜发才),
Yanbo Liang, Yaniv Blumenfeld, Yash Gaurkar, Yicheng Fan, Yong Tang, Yongjoon
Lee, Yuan (Terry) Tang, Yuxin Wu, zldrobit

# Release 1.12.0

## Major Features and Improvements

// DataType value of DT_INVALID signifies that we don't want to
// check the data type.
typedef std::pair<string, DataType> IOSpec;

const char* kFeedStackToString = "File \"feed.cc\", line 10, in alpha";
const char* kNegStackToString = "File \"neg.cc\", line 15, in beta";

std::vector<IOSpec> M(const std::initializer_list<string>& names) {
  std::vector<IOSpec> v;
  for (const string& name : names) {
    v.push_back(IOSpec(name, DT_INVALID));

        'details.')
  config_info_line('mkl', 'Build with MKL support.')
  config_info_line(
      'mkl_aarch64',
      'Build with oneDNN and Compute Library for the Arm Architecture (ACL).')
  config_info_line('monolithic', 'Config for mostly static monolithic build.')
  config_info_line('numa', 'Build with NUMA support.')
  config_info_line(
      'dynamic_kernels',