*   `tf.lite`
    *   Enabled the new MLIR-based quantization backend by default
        *   The new backend is used for 8 bits full integer post-training
            quantization
        *   The new backend removes the redundant rescales and fixes some bugs
            (shared weight/bias, extremely small scales, etc)
        *   Set `experimental_new_quantizer` in tf.lite.TFLiteConverter to False

  }

  // We may need to allocate zero inputs for trainable dtypes we don't have JVPs
  // for. Make sure they get cleaned up.
  std::vector<Gradient*> new_zeros;
  auto delete_new_zeros = gtl::MakeCleanup([&new_zeros, this] {
    for (Gradient* tensor : new_zeros) {
      this->vspace_.DeleteGradient(tensor);
    }
  });
  std::vector<Gradient*> in_grads;
  in_grads.reserve(input_tensors.size());

TFE_MonitoringCounter0* TFE_MonitoringNewCounter0(const char* name,
                                                  TF_Status* status,
                                                  const char* description) {
  auto* result = new TFE_MonitoringCounter0({name, description});
  tsl::Set_TF_Status_from_Status(status, result->counter->GetStatus());
  if (!result->counter->GetStatus().ok()) {
    delete result;
    return nullptr;
  }
  return result;

  return new SqrtGradientFunction(op.outputs[0]);
}

GradientFunction* NegRegisterer(const ForwardOperation& op) {
  return new NegGradientFunction;
}

GradientFunction* SubRegisterer(const ForwardOperation& op) {
  return new SubGradientFunction;
}

GradientFunction* MulRegisterer(const ForwardOperation& op) {
  return new MulGradientFunction(op.inputs);
}

 public:
  explicit GraphContext(const char* name)
      : TracingContext(kGraph),
        graph_(new TF_Graph(), TF_DeleteGraph),
        name_(name) {}

  void Release() override { delete this; }

  TracingOperation* CreateOperation() override {
    return new GraphOperation(graph_.get());
  }

  Status AddParameter(DataType dtype, const PartialTensorShape& shape,

#
# Usage: rename_and_verify_wheels.sh
# This script is aware of TFCI_ variables, so it doesn't need any arguments.
# Puts new wheel through auditwheel to rename and verify it, deletes the old
# one, checks the filesize, and then ensures the new wheel is installable.
set -euxo pipefail

cd "$TFCI_OUTPUT_DIR"

# Move extra wheel files somewhere out of the way. This script

                  TF_Status* status) {
  tensorflow::ImmediateExecutionOperation* new_op =
      tensorflow::unwrap(ctx)->CreateOperation();
  status->status = new_op->Reset(op_or_function_name, nullptr);
  if (!status->status.ok()) {
    new_op->Release();
    new_op = nullptr;
  }
  return tensorflow::wrap(new_op);
}

void TFE_DeleteOp(TFE_Op* op) {
  if (op == nullptr) {
    return;
  }

  # Extract any org/ files under src/main/java
  (mkdir -p src/main/java && cd src/main/java && jar -xf "${tmp_dir}/orig.jar" org/)
  # Repackage src/
  jar -cMf "${tmp_dir}/new.jar" src
  popd
  cp "${tmp_dir}/new.jar" "${dest_jar}"
  rm -rf "${tmp_dir}"
}
DIR=$1
TARBALL_SUFFIX=$2
mkdir -p "$DIR"
cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"

  ops->filesystem_ops->cleanup = tf_gcs_filesystem::Cleanup;
  ops->filesystem_ops->new_random_access_file =
      tf_gcs_filesystem::NewRandomAccessFile;
  ops->filesystem_ops->new_writable_file = tf_gcs_filesystem::NewWritableFile;
  ops->filesystem_ops->new_appendable_file =
      tf_gcs_filesystem::NewAppendableFile;
  ops->filesystem_ops->new_read_only_memory_region_from_file =
      tf_gcs_filesystem::NewReadOnlyMemoryRegionFromFile;

  ///   * Might use any other error value for `status` to signal other errors.
  ///
  /// DEFAULT IMPLEMENTATION: Reads from `src` and writes to `dst`. Needs
  /// `new_random_access_file` and `new_writable_file`.
  void (*copy_file)(const TF_Filesystem* filesystem, const char* src,
                    const char* dst, TF_Status* status);

  /// Checks if `path` exists.
  ///