return nullptr;
}

void TFE_ContextAddFunctionDef(TFE_Context* ctx,
                               const char* serialized_function_def, size_t size,
                               TF_Status* status) {
  tensorflow::FunctionDef function_def;
  if (!function_def.ParseFromArray(serialized_function_def, size)) {
    status->status =
        tensorflow::errors::InvalidArgument("Invalid FunctionDef proto");
    return;
  }

    assertEquals(expectedSize, multimap.size());

    int size = 0;
    for (Entry<K, V> entry : multimap.entries()) {
      assertTrue(multimap.containsEntry(entry.getKey(), entry.getValue()));
      size++;
    }
    assertEquals(expectedSize, size);

    int size2 = 0;
    for (Entry<K, Collection<V>> entry2 : multimap.asMap().entrySet()) {
      size2 += entry2.getValue().size();
    }
    assertEquals(expectedSize, size2);

	"github.com/dustin/go-humanize"
	"github.com/minio/minio/internal/disk"
)

// Block sizes constant.
const (
	SmallBlock  = 32 * humanize.KiByte  // Default r/w block size for smaller objects.
	MediumBlock = 128 * humanize.KiByte // Default r/w block size for medium sized objects.
	LargeBlock  = 1 * humanize.MiByte   // Default r/w block size for normal objects.
)

// aligned sync.Pool's
var (
	ODirectPoolLarge = sync.Pool{

    std::vector<TF_Output> tf_outputs(inputs.size());
    for (int i = 0; i < inputs.size(); i++) {
      GraphTensor* t = dyn_cast<GraphTensor>(inputs[i]);
      if (!t) {
        return tensorflow::errors::InvalidArgument(
            "Unable to cast input to GraphTensor");
      }
      tf_outputs[i] = t->output_;
    }
    TF_AddInputList(op_.get(), tf_outputs.data(), tf_outputs.size());
    return absl::OkStatus();
  }

    const char* attr_name, absl::Span<string const> values) {
  std::vector<const char*> raw_strs;
  std::vector<size_t> lengths;
  raw_strs.reserve(values.size());
  lengths.reserve(values.size());
  for (const auto& s : values) {
    raw_strs.emplace_back(s.data());
    lengths.emplace_back(s.size());
  }
  return SetAttrStringList(attr_name,
                           reinterpret_cast<const void**>(raw_strs.data()),

  std::vector<int64_t> input_ids(inputs.size());
  std::vector<tensorflow::DataType> input_dtypes(inputs.size());
  for (int i = 0; i < inputs.size(); i++) {
    input_ids[i] = ToId(inputs[i]);
    input_dtypes[i] = inputs[i]->DataType();
  }
  std::vector<TapeTensor> tape_tensors;
  tape_tensors.reserve(outputs.size());
  for (auto t : outputs) {
    tape_tensors.push_back(TapeTensor(t));
  }

	r := bytes.NewReader(buf)

	off, length := int64(0), r.Size()
	if rs != nil {
		off, length, err = rs.GetOffsetLength(r.Size())
		if err != nil {
			return nil, err
		}
	}
	r.Seek(off, io.SeekStart)

	return NewGetObjectReaderFromReader(io.LimitReader(r, length), ObjectInfo{
		Bucket:      bucket,
		Name:        object,
		Size:        r.Size(),
		IsLatest:    true,
		ContentType: string(mimeXML),

			return sizeSummary{}, errSkipFile
		}

		sizeS := sizeSummary{}
		for _, tier := range globalTierConfigMgr.ListTiers() {
			if sizeS.tiers == nil {
				sizeS.tiers = make(map[string]tierStats)
			}
			sizeS.tiers[tier.Name] = tierStats{}
		}
		if sizeS.tiers != nil {
			sizeS.tiers[storageclass.STANDARD] = tierStats{}
			sizeS.tiers[storageclass.RRS] = tierStats{}
		}

			sizeS.failedCount++
		case replication.Completed, replication.CompletedLegacy:
			tgtSizeS.replicatedSize += oi.Size
			tgtSizeS.replicatedCount++
			sizeS.replicatedSize += oi.Size
			sizeS.replicatedCount++
		}
		sizeS.replTargetStats[arn] = tgtSizeS
	}

	if oi.ReplicationStatus == replication.Replica {
		sizeS.replicaSize += oi.Size
		sizeS.replicaCount++
	}
}

    assertFalse(
        "Lists of different sizes should not be equal.",
        getList().equals(new ArrayList<E>(fewerElements)));
  }

  public void testEquals_longerList() {
    Collection<E> moreElements = getSampleElements(getNumElements() + 1);
    assertFalse(
        "Lists of different sizes should not be equal.",
        getList().equals(new ArrayList<E>(moreElements)));
  }