// TODO(ashankar): Use gmock matchers instead?
// (https://github.com/google/googletest/blob/master/googlemock/docs/CookBook.md#writing-new-parameterized-matchers-quickly)
// That will require setting up the tensorflow build with gmock.
#define EXPECT_TF_META(attr_name, expected_list_size, expected_type, \
                       expected_total_size)                          \

# limitations under the License.
# ==============================================================================

name: OSV-Scanner Scheduled Scan

on:
  schedule:
    - cron: 0 4 * * 1

permissions:
  # Require writing security events to upload SARIF file to security tab
  security-events: write
  # Only need to read contents
  contents: read

jobs:
  scan-scheduled:
    if: github.repository == 'tensorflow/tensorflow'

  for (const tensorflow::Edge* edge : control_edges) {
    graph->graph.RemoveControlEdge(edge);
  }
}

void TF_SetRequireShapeInferenceFns(TF_Graph* graph, bool require) {
  mutex_lock l(graph->mu);
  graph->refiner.set_require_shape_inference_fns(require);
}

void TF_ExtendSession(TF_Session* session, TF_Status* status) {
  ExtendSessionGraphHelper(session, status);
  session->extend_before_run = false;
}

   `release_updater.sh`

5) Run the `updater.sh` shell script. \
   If the base requirements file hasn't yet been updated to account for the new
   Python version, which will require different versions for at least some
   dependencies, it will need to be updated now, for the script to run
   successfully.

6) A new `requirements_lock_3_11.txt` file should appear under the root of the

  // otherwise be the name of this custom device. Ops are placed onto a custom
  // device if any of their inputs are on that custom device, but custom devices
  // are free to set a bad status in order to require explicit placement.
  void (*execute)(const TFE_Op* op, int* num_outputs,
                  TFE_TensorHandle** outputs, TF_Status* s, void* device_info);

  // Method to delete a device.

*   Include unit tests when you contribute new features, as they help to a)
    prove that your code works correctly, and b) guard against future breaking
    changes to lower the maintenance cost.
*   Bug fixes also generally require unit tests, because the presence of bugs
    usually indicates insufficient test coverage.
*   Keep API compatibility in mind when you change code in core TensorFlow,
    e.g., code in

# limitations under the License.
# ==============================================================================
# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then

  # scripts. By setting "expand_aliases", we enable alias expansion in
  # non-interactive shells as well.
  shopt -s expand_aliases
else
  echo '==TFCI==: Error: Cannot find path to grealpath or gstat'
  echo 'TF CI scripts require GNU core utilties to be installed. Please make'
  echo 'sure they are present on your system and try again.'
  exit 1
fi

# "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use

      const auto func_op = GetFunc(ctx, default_value.func(), status);
      if (!status->status.ok()) return;
      // TODO(nareshmodi): TFE_OpSetAttrFunction and TFE_OpSetAttrFunctionList
      // require TFE_Op* and just convert it internally a NameAttrValue, so
      // consider adding an overload to the C API to make this case easier.
      TFE_OpSetAttrFunction(op, attr_name, func_op);
      TFE_DeleteOp(func_op);

    * If the model is larger than 2GB, then we also require "exclude_conversion_metadata" flag to be set

### Major Features and Improvements