// Get transpose attrs
    bool t_a;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_a", &t_a));

    bool t_b;
    TF_RETURN_IF_ERROR(forward_attrs_.Get("transpose_b", &t_b));

    // Conj each input
    AbstractTensorHandle* conj_output;
    std::string name = "Conj_A_MatMul_Grad";
    TF_RETURN_IF_ERROR(
        SafeConj(ctx, forward_inputs_[0], &conj_output, name.c_str()));

    bool clear_existing_contexts) {
#if defined(IS_MOBILE_PLATFORM)
  status->status = tensorflow::errors::Unimplemented(
      "TFE_ContextSetServerDef not supported on mobile");
#else   // !defined(IS_MOBILE_PLATFORM)
  tensorflow::ServerDef server_def;
  if (!server_def.ParseFromArray(proto, proto_len)) {
    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  }

# limitations under the License.
# ==============================================================================
if [[ "$TFCI_DOCKER_PULL_ENABLE" == 1 ]]; then
  # Simple retry logic for docker-pull errors. Sleeps for 15s if a pull fails.
  # Pulling an already-pulled container image will finish instantly, so
  # repeating the command costs nothing.
  docker pull "$TFCI_DOCKER_IMAGE" || sleep 15

    *   One MUST NOT access `tensorflow::errors::Code` fields. Accessing
        `tensorflow::error::Code` fields is fine.
        *   Use the constructors such as `tensorflow::errors:InvalidArgument` to
            create status using an error code without accessing it.
        *   Use the free functions such as
            `tensorflow::errors::IsInvalidArgument` if needed.

        ":abstract_operation",
        ":c_api_unified_internal",
        "//tensorflow/c/experimental/gradients/tape:tape_operation",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
    ],
)

cc_library(
    name = "gradients_internal",
    srcs = [
        "gradients.cc",
    ],
    hdrs = [
        "gradients.h",
        "gradients_internal.h",
    ],

 public:
  // Returns tensor dtype.
  virtual tensorflow::DataType DataType() const = 0;

  // Returns the status of the tensor handle. If it is a tfrt::TensorHandle,
  // the tensor handle can be an error and return non-OK status.
  virtual tensorflow::Status TensorHandleStatus() const;

  // Returns tensor shape. If tensor has unknown rank, shape remains untouched.
  virtual tensorflow::Status Shape(

  // Block until all pending nodes are finished.
  virtual Status AsyncWait() = 0;

  // Add a function (serialized FunctionDef protocol buffer) so that it can
  // be executed as an op. Return error if the function with the same name
  // already exists.
  virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;

  // Notifies about the function removal.

# "anything greater than" is added by the script.
if [[ "$TFCI_WHL_SIZE_LIMIT_ENABLE" == "1" ]] && [[ -n "$(find . -iname "*.whl" -size "+$TFCI_WHL_SIZE_LIMIT")" ]]; then
  echo "Error: Generated wheel is too big! Limit is $TFCI_WHL_SIZE_LIMIT"
  echo '(search for TFCI_WHL_SIZE_LIMIT to change it)'
  ls -sh *.whl
  exit 2
fi

# Quick install checks
venv=$(mktemp -d)

  # scripts. By setting "expand_aliases", we enable alias expansion in
  # non-interactive shells as well.
  shopt -s expand_aliases
else
  echo '==TFCI==: Error: Cannot find path to grealpath or gstat'
  echo 'TF CI scripts require GNU core utilties to be installed. Please make'
  echo 'sure they are present on your system and try again.'
  exit 1
fi

        "//tensorflow/c:tf_status_helper",
        "//tensorflow/c:tf_tensor_internal",
        "//tensorflow/cc/saved_model:loader",
        "//tensorflow/compiler/mlir/lite/metrics:error_collector",
        "//tensorflow/compiler/mlir/lite/python:flatbuffer_to_mlir",
        "//tensorflow/compiler/mlir/lite/python:graphdef_to_tfl_flatbuffer",