# in TensorFlow are manually enumerated rather than generated automatically.
do_external_licenses_check(){
  BUILD_TARGET="$1"
  LICENSES_TARGET="$2"

  # grep patterns for targets which are allowed to be missing from the licenses
  cat > $BATS_TEST_TMPDIR/allowed_to_be_missing <<EOF
@absl_py//absl
@bazel_tools//platforms
@bazel_tools//third_party/
@bazel_tools//tools
@local
@com_google_absl//absl
@pybind11_abseil//pybind11_abseil

compile_pip_requirements_3_9(
    name = "requirements_3_9",
    extra_args = ["--allow-unsafe"],
    requirements_in = REQUIREMENTS_FILE_NAME,
    requirements_txt = "requirements_lock_3_9.txt",
)

compile_pip_requirements_3_10(
    name = "requirements_3_10",
    extra_args = ["--allow-unsafe"],
    requirements_in = REQUIREMENTS_FILE_NAME,
    requirements_txt = "requirements_lock_3_10.txt",
)

setup_file() {
    bazel version  # Start the bazel server

    # Fixes "fatal: detected dubious ownership in repository" for Docker.
    git config --system --add safe.directory '*'
    git config --system protocol.file.allow always

    # Note that you could generate a list of all the affected targets with e.g.:
    # bazel query $(paste -sd "+" $BATS_FILE_TMPDIR/changed_files) --keep_going

  TF_Operation* plusB = Add(plus2, b, graph, s, "plusB");
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Setup a session and a partial run handle.  The partial run will allow
  // computation of A + 2 + B in two phases (calls to TF_SessionPRun):
  // 1. Feed A and get (A+2)
  // 2. Feed B and get (A+2)+B
  TF_SessionOptions* opts = TF_NewSessionOptions();

      // that add a node to the graph (like TF_FinishOperation).
      if (!g->name_map.insert(std::make_pair(n->name(), n)).second) {
        status->status = tensorflow::errors::Internal(
            "BUG: The API allowed construction of a graph with duplicate node "
            "names (",
            n->name(),
            "). This is a bug. Please file an issue at "
            "https://github.com/tensorflow/tensorflow/issues.");

    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.

          name = "requirements_3_11",
          extra_args = ["--allow-unsafe"],
          requirements_in = "requirements.in",
          requirements_txt = "requirements_lock_3_11.txt",
      )
   ```

   ```
      compile_pip_requirements_3_11(
          name = "requirements_3_11_release",
          extra_args = [
              "--allow-unsafe",
              "-P keras-nightly",
              "-P tb-nightly",

  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](https://code.google.com/legal/corporate-cla-v1.0.html).

// Eager Executor APIs.
typedef struct TFE_Executor TFE_Executor;

// Creates a new eager Executor. Nodes in one executor are guaranteed to be
// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops

#include "tensorflow/c/tf_status.h"
#include "xla/pjrt/c/pjrt_c_api.h"

// --------------------------------------------------------------------------
// C API for device. The API is under active development and eventually
// should allow registering a plugin device with TensorFlow.

#ifdef __cplusplus
extern "C" {
#endif

// TF_Device is a C wrapper to the C++ TF Device class. This is to be passed
// through TF_OpKernelContext, and is opaque to plugin.