#   TF_BISECT_BAD: First bad commit (e.g. commit from the first failing job)
#   TF_BISECT_SCRIPT: The build script path relative to the TF root dir, e.g.
#     ci/official/wheel.sh
#   TFCI: The env config path, relative to the TF root dir, e.g.
#     ci/official/envs/an_env_config
#
# Note that you can combine bisect.sh with any.sh to bisect a single test:
#
#   export TFCI=...

importing "tf.foo" followed by "tf.bar" won't reveal that "tf.bar" depends on
"tf.foo" being imported first.

The `_api/v2/api_packages.txt` file is generated during the TensorFlow API v2
init files creation process and is subsequently stored in the wheel file after
the build. It also contains a few paths that cannot be directly imported. These
paths point to attributes or sub-modules within a module's namespace, but they

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
# Re-direct all links in $1 that are relative to be canonical

BASE="$1"
find "${BASE}" -type l | \
  while read l ; do
    if [[ "$(readlink "$l")" == \.\./* ]]; then
      CANONICAL="$(readlink "$l")";
      rm "$l";
      ln -s "${CANONICAL}" "$l"

//
// A context is responsible for creating key objects such as Tensors,
// TensorHandles & Operations.
class ImmediateExecutionContext : public AbstractContext {
 public:
  // Optimized scalar creation functions
  virtual AbstractTensorInterface* CreateInt64Scalar(int64_t value) = 0;
  virtual AbstractTensorInterface* CreateUint64Scalar(uint64 value) = 0;
  virtual AbstractTensorInterface* CreateInt32Scalar(int32_t value) = 0;

  TF_Operation* neg = Neg(feed, func_graph_, s_, "neg");
  Define(-1, {}, {feed}, {neg}, {"negation"});

  // Use, run, and verify
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({func_feed});
  Run({{func_feed, Int32Tensor(3)}}, func_op, -3);
  VerifyFDef({"neg"}, {{"negation_0", DT_INT32}}, {{"negation", DT_INT32}},

    *   Raw TensorFlow functions can now be used in conjunction with the Keras
        Functional API during model creation. This obviates the need for users
        to create Lambda layers in most cases when using the Functional API.
        Like Lambda layers, TensorFlow functions that result in Variable
        creation or assign ops are not supported.
    *   Add support for passing list of lists to the `metrics` argument in Keras

  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph

# BEGIN TF REMOTE BUILD EXECUTION OPTIONS
# Options when using remote execution
# WARNING: THESE OPTIONS WONT WORK IF YOU DO NOT HAVE PROPER AUTHENTICATION AND PERMISSIONS

# Allow creation of resultstore URLs for any bazel invocation
build:resultstore --google_default_credentials
build:resultstore --bes_backend=buildeventservice.googleapis.com
build:resultstore --bes_instance_name="tensorflow-testing"

  control_output_nodes.reserve(ncontrol_outputs);
  for (int i = 0; i < ncontrol_outputs; ++i) {
    control_output_nodes.push_back(&control_outputs[i]->node);
  }

  // Do the actual function creation.
  DCHECK(append_hash_to_fn_name <= 1);
  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,

// * size_t is used to represent byte sizes of objects that are
//   materialized in the address space of the calling process.
// * int is used as an index into arrays.
// * Deletion functions are safe to call on nullptr.
//
// Questions left to address:
// * Might at some point need a way for callers to provide their own Env.
// * Maybe add TF_TensorShape that encapsulates dimension info.
//