std::vector<TF_Output> out;
    for (auto op : ops) {
      out.push_back({op, 0});
    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,
              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {

	VWMACCUSVX	V2, X10, V0, V3			// d76125f8

	// 31.11.15: Vector Integer Merge Instructions
	VMERGEVVM	V1, V2, V0, V3			// d781205c
	VMERGEVXM	X10, V2, V0, V3			// d741255c
	VMERGEVIM	$15, V2, V0, V3			// d7b1275c

	// 31.11.16: Vector Integer Move Instructions
	VMVVV		V2, V3				// d701015e
	VMVVX		X10, V3				// d741055e
	VMVVI		$15, V3				// d7b1075e

	// 31.12.1: Vector Single-Width Saturating Add and Subtract

        return connectionManager;
    }

    /**
     * Builds the SSL socket factory for HTTPS connections.
     *
     * @param httpClientBuilder The HTTP client builder
     * @return The configured SSL socket factory
     */
    protected LayeredConnectionSocketFactory buildSSLSocketFactory(final HttpClientBuilder httpClientBuilder) {

    EXPECT_TRUE(found);
    TF_DeleteBuffer(op_list_buffer);
  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  absl::Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));

absl-py==2.3.0 \
    --hash=sha256:9824a48b654a306168f63e0d97714665f8490b8d89ec7bf2efc24bf67cf579b3 \
    --hash=sha256:d96fda5c884f1b22178852f30ffa85766d50b99e00775ea626c23304f582fc4f
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e

//
//    ncontrol_outputs: Number of control outputs of the function.
//    control_outputs: vector of TF_Operation objects to be marked as control
//      outputs of the function. Operations marked as control outputs are
//      guaranteed to execute.
//    control_output_names: Optional. If not nullptr, vector of strings, one
//      per control output, with their names to be added to the function's
//      OpDef.

build:release_arm64_linux --crosstool_top="@ml2014_clang_aarch64_config_aarch64//crosstool:toolchain"
build:release_arm64_linux --config=mkl_aarch64_threadpool
build:release_arm64_linux --copt=-flax-vector-conversions
test:release_arm64_linux --flaky_test_attempts=3

build:release_cpu_macos --config=avx_linux

# Base build configs for macOS
build:release_macos_base --config=clang_local

absl-py==2.2.2 \
    --hash=sha256:bf25b2c2eed013ca456918c453d687eab4e8309fba81ee2f4c1a6aa2494175eb \
    --hash=sha256:e5797bc6abe45f64fd95dc06394ca3f2bedf3b5d895e9da691c9ee3397d70092
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \
    --hash=sha256:95c30d87a6c2cf89aa628b87398466840f0ad8652f88eb173125a6df8533fb8d \
    --hash=sha256:fb503b9e2fdd05609fbf557b916b4a7824171203701660f0c55bbf5a7a68713e

		// xl.meta has "inlined data" we prefer writing O_DIRECT and then doing
		// fdatasync() at the end instead of opening the file with O_DSYNC.
		//
		// This is an optimization mainly to ensure faster I/O.
		if len(b) > xioutil.DirectioAlignSize {
			r := bytes.NewReader(b)
			return s.writeAllDirect(ctx, filePath, r.Size(), r, flags, skipParent, true)
		}
		w, err = s.openFileSync(filePath, flags, skipParent)