- name: "Upload artifact"
        uses: actions/upload-artifact@83fd05a356d7e2593de66fc9913b3002723633cb # v3.1.1
        with:
          name: SARIF file
          path: results.sarif
          retention-days: 5

      # Upload the results to GitHub's code scanning dashboard.
      - name: "Upload to code-scanning"
        uses: github/codeql-action/upload-sarif@896079047b4bb059ba6f150a5d87d47dde99e6e5 # v2.11.6
        with:

            This issue is stale because it has been open for 7 days with no activity.
            It will be closed if no further activity occurs. Thank you.
          close-issue-message: >
            This issue was closed because it has been inactive for 7 days since being marked as stale.
            Please reopen if you'd like to work on this further.
          days-before-pr-stale: 14
          days-before-pr-close: 14

```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

### Resource allocation

A denial of service caused by one model could bring down the entire server, but
we don't consider this as a vulnerability, given that models can exhaust
resources in many different ways and solutions exist to prevent this from
happening (e.g., rate limits, ACLs, monitors to restart broken servers).

### Model sharing

If the multitenant design allows sharing models, make sure that tenants and

  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];
  }

  dlm_tensor->dl_tensor.shape = shape_arr->data();
  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.

*   Added `keras.export.ExportArchive` class for low-level customization of the process of exporting SavedModel artifacts for inference. Both ways of exporting models are based on `tf.function` tracing and produce a TF program composed of TF ops. They are meant primarily for environments where the TF runtime is available, but not the Python interpreter, as is typical for production with TF Serving....

#include "tensorflow/c/eager/abstract_op_attrs.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/core/framework/attr_value.pb.h"

// An equivalent of a tensorflow::NameAttrList protocol buffer, but used in ways
// that sometimes do not require serialization.
typedef struct TFE_OpAttrs TFE_OpAttrs;

typedef struct TFE_Context TFE_Context;
typedef struct TFE_Op TFE_Op;

namespace tensorflow {

    TF_Status* status) {
  absl::optional<std::vector<MaybeParallelTensorOwned>> result;
  // TODO(allenl): We should remove "TPU" from these op names at the very least,
  // or consider other ways of packing/unpacking parallel tensors.
  if (operation_name == std::string("TPUReplicatedInput")) {
    // Special-cased operation for packing per-device tensors into one parallel
    // tensor.