*   `tf.SavedModel`

    *   Introduced class method `tf.saved_model.experimental.Fingerprint.from_proto(proto)`, which can be used to construct a `Fingerprint` object directly from a protobuf.

// On success, returns a set of TF_Function instances from `text_proto` of
// GraphDef type. These functions must be deleted by calling TF_DeleteFunction.
//
// If `mutate_proto_func` is non-NULL, run it over each FunctionDef proto,
// before creating a TF_Function out of the possibly mutated proto.
static std::vector<UniqueFuncPtr> CreateFunctionsFromTextProto(
    const char* text_proto,

                                                 TF_Tensor* tensor,
                                                 TF_Status* status);
// Create a serialized tensorflow.ServerDef proto.
TF_Buffer* TFE_GetServerDef(const char* text_proto, TF_Status* status);

TF_CAPI_EXPORT extern void TF_MakeInternalErrorStatus(TF_Status* status,
                                                      const char* errMsg);

cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"
cp_normalized_srcjar bazel-bin/tensorflow/java/libtensorflow-src.jar "${DIR}/libtensorflow-src.jar"

    choco install openjdk11; \
    choco install peazip; \
    choco install peazip.install; \
    choco install peazip.portable; \
    choco install php --version 7.2.0; \
    choco install protoc --version 3.2.0; \
    choco install ruby --version 2.5.0.1; \
    choco install swig --version 3.0.9; \
    choco install sysinternals; \
    choco install unrar; \
    choco install unzip; \

# opts in to modular op registration support by default.
build --define framework_shared_object=true
build --define tsl_protobuf_header_only=true

build --define=use_fast_cpp_protos=true
build --define=allow_oversize_protos=true

build --spawn_strategy=standalone
build -c opt

# Make Bazel print out all options from rc files.
build --announce_rc

//tensorflow/core:image_testdata
//tensorflow/core/kernels/cloud:bigquery_reader_ops
//tensorflow/python:extra_py_tests_deps
//tensorflow/python:mixed_precision
//tensorflow/python:tf_optimizer
//tensorflow/python:compare_test_proto_py
//tensorflow/python/framework:test_file_system.so
//tensorflow/python/debug:grpc_tensorflow_server.par
//tensorflow/python/feature_column:vocabulary_testdata
//tensorflow/python/util:nest_test_main_lib

// `proto` must point to an array of `proto_len` bytes representing a
// binary-serialized TensorShapeProto.
TF_CAPI_EXPORT extern void TF_SetAttrTensorShapeProto(
    TF_OperationDescription* desc, const char* attr_name, const void* proto,
    size_t proto_len, TF_Status* status);
// `protos` and `proto_lens` must point to arrays of length `num_shapes`.
// `protos[i]` must point to an array of `proto_lens[i]` bytes

# proto-file: devtools/build_cleaner/proto/actions.proto
# proto-message: ActionSpecs

# Python rules should not have more than one source file.
action_spec {
  action: CHECK_FILE_COUNT
  file_count_params {
    rule_selector {
      rule_kind_regex: "^.*py(type)?(_strict)?_(binary|library|test).*$"
      generator_function_regex: "^(?!boq_header)$"
    }
    max_source_count: 1
  }

                                  const void* proto, size_t proto_len,
                                  TF_Status* status) {
  tensorflow::AttrValue attr_value;
  if (!attr_value.ParseFromArray(proto, proto_len)) {
    status->status = InvalidArgument(
        "Unparseable AttrValue proto passed to "
        "TF_FunctionSetAttrValueProto");
    return;
  }