@SuppressWarnings("unused")
    @Test
    public void testIterable() throws Exception {
        final Vector<String> vector = new Vector<String>();
        vector.add("a");
        vector.add("b");
        int count = 0;
        for (final String s : iterable(vector.elements())) {
            ++count;
        }
        assertThat(count, is(2));
    }

    /**
     * Test method for

  Status Shape(const std::vector<int64_t>** shape) const;
  TF_DataType dtype() const { return dtype_; }

  // Sets its output argument to a summary of the values of this tensor on every
  // component device.
  Status SummarizeValue(std::string& summary);

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {

  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(
    const std::vector<PartialTensorShape>& expected_output_shapes,
    TF_Status* status) const {
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result;
  // Compute per-device per-output tensors
  std::vector<std::vector<TensorHandlePtr>> per_device_output_tensors;

            if ( mechs != null ) {
                ASN1EncodableVector vector = new ASN1EncodableVector();
                for ( int i = 0; i < mechs.length; i++ ) {
                    vector.add(mechs[ i ]);
                }
                fields.add(new DERTaggedObject(true, 0, new DERSequence(vector)));
            }
            int ctxFlags = getContextFlags();
            if ( ctxFlags != 0 ) {

  device->delete_device = &DeleteParallelDevice;
  device->execute = &ParallelDeviceExecute;
  std::vector<std::string> underlying_devices_vector;
  underlying_devices_vector.reserve(num_underlying_devices);
  for (int device_index = 0; device_index < num_underlying_devices;
       ++device_index) {
    underlying_devices_vector.push_back(underlying_devices[device_index]);
  }
  std::unique_ptr<ParallelDevice> parallel_device(

	"bytes"
	"compress/bzip2"
	"crypto/md5"
	"errors"
	"fmt"
	"io"
	"math"
	"os"
	"path"
	"reflect"
	"strconv"
	"strings"
	"testing"
	"time"
)

func TestReader(t *testing.T) {
	vectors := []struct {
		file    string    // Test input file
		headers []*Header // Expected output headers
		chksums []string  // MD5 checksum of files, leave as nil if not checked
		err     error     // Expected error to occur
	}{{

  int num_elems = TF_TensorElementCount(theta_tensor);
  vector<float> theta_data(num_elems);
  memcpy(theta_data.data(), TF_TensorData(theta_tensor),
         TF_TensorByteSize(theta_tensor));

  // Initialize space for the numerical gradient.
  vector<float> dtheta_approx(num_elems);

  // Get theta shape and store in theta_dims.
  int num_dims = TF_NumDims(theta_tensor);
  vector<int64_t> theta_dims(num_dims);

      separated.withSeparator("$", 4);
      fail("Expected UnsupportedOperationException");
    } catch (UnsupportedOperationException expected) {
    }
  }

  public void testBase64() {
    // The following test vectors are specified in RFC 4648 itself
    testEncodingWithSeparators(base64(), "", "");
    testEncodingWithSeparators(base64(), "f", "Zg==");
    testEncodingWithSeparators(base64(), "fo", "Zm8=");

  // Process inputs.
  std::vector<tensorflow::OutputTensor> input_tensors;
  std::unordered_map<const Node*, std::vector<int>> input_nodes;
  status->status = tensorflow::ProcessInputs(fn_body, fn_name, ninputs, inputs,
                                             &input_tensors, &input_nodes);
  if (TF_GetCode(status) != TF_OK) return nullptr;

  // Process outputs.
  std::vector<tensorflow::OutputTensor> output_tensors;

        absl::InlinedVector<const void*, 4> values_vector;
        values_vector.reserve(s_size);
        absl::InlinedVector<size_t, 4> lengths_vector;
        lengths_vector.reserve(s_size);
        for (int i = 0; i < s_size; ++i) {
          const string& v = default_value.list().s(i);
          values_vector.push_back(v.data());
          lengths_vector.push_back(v.size());
        }