TFE_Op* original_var_op = TFE_NewOp(ctx, "VarHandleOp", status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpSetAttrType(original_var_op, "dtype", TF_INT64);
  TFE_OpSetAttrShape(original_var_op, "shape", {}, 0, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  EXPECT_EQ("", std::string(TFE_OpGetDevice(original_var_op, status)));

  TF_Operation* feed4 =
      TF_GraphOperationByName(graph, "imported4/imported2/feed");

  // Check that imported `imported3/scalar` has remapped control dep from
  // original graph and imported control dep
  num_control_inputs = TF_OperationGetControlInputs(
      scalar4, control_inputs, TF_OperationNumControlInputs(scalar4));
  ASSERT_EQ(2, num_control_inputs);

To generate additional operators, extend the lists in this script. Note that
category names correspond to generated source file names, and should be
consistent with the original source files registering each operator. For example
since `REGISTER_OP("MatMul")` appears in ***core/math_ops.cc***, the "MatMul"
operator in the script should be in the "math" category, and it will be

for wheel in /tf/pkg/*.whl; do
  echo "Checking and renaming $wheel..."
  time python3 -m auditwheel repair --plat manylinux2014_aarch64 "$wheel" --wheel-dir /tf/pkg 2>&1 | tee check.txt

  # We don't need the original wheel if it was renamed
  new_wheel=$(grep --extended-regexp --only-matching '/tf/pkg/\S+.whl' check.txt)
  if [[ "$new_wheel" != "$wheel" ]]; then
    rm "$wheel"
    wheel="$new_wheel"
  fi

        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).

namespace tensorflow {

namespace {

// Managing context for the DLManagedTensor, will manage the lifetime of
// DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the
// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  // Method to execute an operation.
  //
  // Arguments provide enough information to reconstruct the original `TFE_Op`,
  // or construct a transformed version, by inspecting the passed `op`.
  //
  // TFE_OpGetDevice(op) records the original placement of the operation. It may
  // be an empty string if no device was explicitly requested, but will

                         off_t size) {
  int src_fd = open(src, O_RDONLY);
  if (src_fd < 0) return -1;

  // When creating file, use the same permissions as original
  mode_t open_mode = mode & (S_IRWXU | S_IRWXG | S_IRWXO);

  // O_WRONLY | O_CREAT | O_TRUNC:
  //   Open file for write and if file does not exist, create the file.
  //   If file exists, truncate its size to 0.

*   Added utility `tf.keras.utils.to_ordinal` to convert class vector to ordinal regression / classification matrix.

### Bug Fixes and Other Changes

*   N/A

## Security