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

      - 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 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

  }
  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.

#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 {

## Conflict Resolution

Conflicts in an open source project can take many forms, from someone having a bad day and using harsh and hurtful language in the issue queue, to more serious instances such as sexist/racist statements or threats of violence, and everything in between.

### 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

        with:
          registry: gcr.io
          username: _json_key
          password: ${{ secrets.GCP_CREDS }}
      -
        name: Grab the date to do cache busting (assumes same day OK to keep)
        run: |
          echo "DATE=$(date +'%Y-%m-%d')" >> "$GITHUB_OUTPUT"
        id: date
      -

*   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....

          echo "TF_VERSION=${major_version}.${minor_version}" >> "$GITHUB_OUTPUT"
          # Also get the current date to do cache busting. Assumes one day
          # is an ok range for rebuilds
          echo "DATE=$(date +'%Y-%m-%d')" >> "$GITHUB_OUTPUT"
        id: tf-version
      -
        name: Build and push
        id: docker_build