--hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \

    --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \

    --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \

    control_output_nodes.push_back(&control_outputs[i]->node);
  }

  // Do the actual function creation.
  DCHECK(append_hash_to_fn_name <= 1);
  tensorflow::FunctionDef fdef;
  status->status = tensorflow::GraphToFunctionDef(
      fn_body->graph, fn_name, append_hash_to_fn_name != 0,
      /*set_stateful_from_nodes=*/true,
      /*copy_placeholder_attrs_from_nodes=*/true, body_nodes, input_tensors,

          # metadata. gcr.io helpfully includes it in the header of the response
          # as docker-content-digest: sha256:[digest]. Note we use egrep to
          # match exactly sha256:<hash> because curl may include a ^M symbol at
          # the end of the line.
          # See https://cloud.google.com/architecture/using-container-images#exploring_image_manifests_digests_and_tags

build:rbe_linux_cuda_nvcc --config=nvcc_clang
build:rbe_linux_cuda_nvcc --repo_env TF_NCCL_USE_STUB=1

build:rbe_win_base --config=rbe_base
build:rbe_win_base --shell_executable=C:\\tools\\msys64\\usr\\bin\\bash.exe
build:rbe_win_base --remote_instance_name=projects/tensorflow-testing/instances/windows
# Don't build the python zip archive in the RBE build.
build:rbe_win_base --remote_download_minimal

    --hash=sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574 \
    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
    --hash=sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519 \
    --hash=sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898 \
    --hash=sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269 \

        ":tape",
        "//tensorflow/core/common_runtime/eager:attr_builder",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
        "@com_google_absl//absl/container:flat_hash_map",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "unified_api_testutil",
    testonly = 1,
    srcs = [
        "unified_api_testutil.cc",
    ],
    hdrs = [

#include "tensorflow/c/eager/c_api_experimental.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/match.h"
#include "absl/time/time.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"
#include "tensorflow/c/eager/tfe_context_internal.h"

        to `tf.string_strip`), `tf.strings.substr`, `tf.strings.to_hash_bucket`
        (corresponds to `tf.string_to_hash_bucket`),
        `tf.strings.to_hash_bucket_fast` (corresponds to
        `tf.string_to_hash_bucket_fast`), `tf.strings.to_hash_bucket_strong`
        (corresponds to `tf.string_to_hash_bucket_strong`).

## Thanks to our Contributors