--enable_bzlmod=false \
    //:requirements_"$VERSION".update
  sed -i '/^#/d' requirements_lock_"$VERSION".txt
  mv requirements_lock_"$VERSION".txt ../../../requirements_lock_"$VERSION".txt
done

// destination tensor to be placed in host memory).
// If async execution is enabled, the copy may be enqueued and the call will
// return "non-ready" handle. Else, this function returns after the copy has
// been done.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(
    TFE_TensorHandle* h, TFE_Context* ctx, const char* device_name,
    TF_Status* status);

// Debugging/Profiling information for TFE_TensorHandle

      # it, and print that list for debugging. Not really clear if this is
      # helpful since the only examples I've seen are enormous.
      bazel query "rdeps(kind(py_test, $(cat $BATS_TEST_TMPDIR/deps)), $dep, 1)"
    done < $BATS_TEST_TMPDIR/missing_deps
    exit 1
  fi
}

# The Python package is not allowed to depend on any CUDA packages.
@test "Pip package doesn't depend on CUDA" {
  bazel cquery \

# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,
# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 

# drops all the bazel dependencies for each py_test; this makes all the tests
# use the wheel's TensorFlow installation instead of the one made available
# through bazel. This must be done in a different root directory, //bazel_pip/...,
# because "import tensorflow" run from the root directory would instead import
# the folder instead of the venv package.
# 

    --target=$target \
    --cache-from "$IMAGE" \
    -t "$IMAGE"  . && break
  done
  final=$?
  if [ $final -ne 0 ]; then
    exit $final
  fi
  set -e

  if [[ -n "$KOKORO_BUILD_ID" ]]; then
    gcloud auth configure-docker
    docker push "$IMAGE"
  fi

  parg=$(echo "${cmd}" |sed 's,.*\(-p[0-9]\).*,\1,')
  if [[ ! "${file}" =~ doxygen && "${cmd}" != \#* ]]; then
    echo "patch ${parg} -s < ${file}"
    patch ${parg} -s < "${file}"
  fi

  # Sources every env, in order, from the comma-separated list "TFCI"
  # Assumes variables will resolve themselves correctly.
  set +u
  for env_file in ${TFCI//,/ }; do
    source "./ci/official/envs/$env_file"
  done
  set -u
  echo '==TFCI==: Evaluated the following TFCI variables from $TFCI:'
  export -p | grep TFCI

  # Load those stored pre-existing TFCI_ vars, if any
  if [[ -s "$FROM_ENV" ]]; then

// to `g`. Once `func`/`grad` is added to `g`, it can be called by creating
// an operation using the function's name.
// Any changes to `func`/`grad` (including deleting it) done after this method
// returns, won't affect the copy of `func`/`grad` in `g`.
// If `func` or `grad` are already in `g`, TF_GraphCopyFunction has no
// effect on them, but can establish the function->gradient relationship

  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });
  done.WaitForNotification();
}

TF_ShapeAndTypeList* TF_NewShapeAndTypeList(int num_items) {