export TF_VER_SUFFIX=$(awk -F\" '/#define TF_VERSION_SUFFIX/ {print $2}' tensorflow/core/public/version.h)
export TF_VER_PYTHON=$(awk -F\' '/_VERSION =/ {print $2}' tensorflow/tools/pip_package/setup.py)

# Derived helper variables.
export TF_VER_SHORT="${TF_VER_MAJOR}.${TF_VER_MINOR}"

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

  // case placement information is lost when resolving the handle.
  //
  // If false, a caller might implement pretty-printing by resolving and
  // iterating over the resulting tensor. This may still be viable if resolving
  // the handle loses information, but `SummarizeValue` would be more precise.
  virtual bool PreferCustomSummarizer() const { return false; }

              "localhost:", tensorflow::testing::PickUnusedPortOrDie())});

  tensorflow::JobDef* job_def2 = cluster_def->add_job();
  job_def2->set_name("worker");

  // Copy over `host:port` at `task_index`
  for (auto task : cluster_server_def.cluster().job(0).tasks()) {
    if (task.first == task_index) {
      job_def2->mutable_tasks()->insert({task.first, task.second});
    }
  }

}

TEST(CAPI, TensorEncodeDecodeStrings) {
  TestEncodeDecode(__LINE__, {});
  TestEncodeDecode(__LINE__, {"hello"});
  TestEncodeDecode(__LINE__,
                   {"the", "quick", "brown", "fox", "jumped", "over"});

  string big(1000, 'a');
  TestEncodeDecode(__LINE__, {"small", big, "small2"});
}

TEST(CAPI, SessionOptions) {
  TF_SessionOptions* opt = TF_NewSessionOptions();
  TF_DeleteSessionOptions(opt);

//tensorflow/python/kernel_tests/signal
//tensorflow/examples
//tensorflow/tools/android
//tensorflow/python/eager/benchmarks
EOF

  # grep patterns for files and targets which don't need to be in the pip
  # package, ever.
  cat > $BATS_TEST_TMPDIR/ignore_these_deps <<EOF
benchmark
_test$
_test.py$
_test_cpu$
_test_cpu.py$
_test_gpu$
_test_gpu.py$
_test_lib$
//tensorflow/cc/saved_model:saved_model_test_files

  if (num_dims > TensorShape::MaxDimensions()) {
    return errors::InvalidArgument("Value specified for `", attr_name, "` has ",
                                   num_dims,
                                   " dimensions which is over the limit of ",
                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

// through custom device implementations).
//
// TODO(allenl): Currently these are black boxes, but we should have some way to
// inspect values. This would let people e.g. copy over most attributes and then
// modify some based on their values.

// A reference to an op's name -> attribute mapping
typedef struct TFE_OpAttrs TFE_OpAttrs;

        -  It could also be a simple python or TF utility that you could easily copy over to your own codebase. In those case, just make it your own!
        -  If you believe it should definitely be a public Keras API, please open a feature request in keras GitHub repo.

    visibility = ["//visibility:public"],
)

# This condition takes precedence over :linux_x86_64
config_setting(
    name = "linux_x86_64_no_sse",
    values = {
        "cpu": "k8",
        "copt": "-mno-sse4.2",
    },
    visibility = ["//visibility:public"],
)

# This condition takes precedence over :linux_x86_64
# TODO(b/290533709): Remove this with PJRT build rule cleanup.
config_setting(