TF_SetStatus(status, TF_OK, "");
    return n;
  };
  tf_gcs_filesystem::RamFileBlockCache cache(block_size, block_size, 0,
                                             fetcher);
  // Fork off thread for parallel read.
  std::unique_ptr<Thread> concurrent(
      Env::Default()->StartThread({}, "concurrent", [block_size, &cache] {
        std::vector<char> out;

            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}

          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository for nightly (skipped for releases)
        if: ${{ github.event_name == 'schedule' }}

1.  Make sure the Issue Template is filled out.
1.  The issue should be related to the repo it is created in.

**Here's why we have this policy:** We want to focus on the work that benefits
the whole community, e.g., fixing bugs and adding features. Individual support
should be sought on Stack Overflow or other non-GitHub channels. It helps us to

            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash
        run: find /home/ubuntu/actions-runner/_work/tensorflow/tensorflow/. -name . -o -prune -exec sudo rm -rf -- {} + || true
      - name: Checkout repository
        uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
// Classes and utilities that work with Graph C API for internal use.
// This includes functions used for optimizer registration and interfaces needed
// for testing.

#ifndef TENSORFLOW_C_EXPERIMENTAL_GRAPPLER_GRAPPLER_INTERNAL_H_

    forward_op_.outputs.push_back(retvals[i]);
  }
  // TODO(b/166669239): This is needed to support AttrBuilder::Get for string
  // attributes. Number type attrs and DataType attrs work fine without this.
  // Consider getting rid of this and making the behavior between number types
  // and string consistent.
  forward_op_.attrs.BuildNodeDef();
  // TODO(b/170307493): Populate skip_input_indices here.

      ReluModel, ReluGradModel, immediate_execution_ctx_.get(), {X.get()},
      UseFunction()));

  // Mathematically, Relu isn't differentiable at `0`. So `gradient_checker`
  // does not work with it.
  AbstractTensorHandlePtr Y;
  {
    AbstractTensorHandle* Y_raw;
    status_ = TestScalarTensorHandle<float, TF_FLOAT>(
        immediate_execution_ctx_.get(), 0.0f, &Y_raw);

      // and hence expect the input to be forwarded to the return tensor.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    } else {
      // In async mode we expect forwarding to work without releasing the input
      // handle since by the time the kernel is executed we have released the
      // handle in the client code.
      EXPECT_EQ(orig_ptr, TF_TensorData(t));
    }
  } else {

  TF_Status* s = TF_NewStatus();
  const char* tags[] = {tensorflow::kSavedModelTagServe};
  TF_Graph* graph = TF_NewGraph();
  // NULL run_options and meta_graph_def should work.
  TF_Session* session = TF_LoadSessionFromSavedModel(
      opt, nullptr, saved_model_dir.c_str(), tags, 1, graph, nullptr, s);
  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  TF_DeleteSessionOptions(opt);