currOffset int64
	h          hash.Hash
	shardSize  int64
	hashBytes  []byte
}

func (b *streamingBitrotReader) Close() error {
	if b.rc == nil {
		return nil
	}
	if closer, ok := b.rc.(io.Closer); ok {
		// drain the body for connection reuse at network layer.
		xhttp.DrainBody(io.NopCloser(b.rc))
		return closer.Close()
	}
	return nil
}

		t.Fatalf("failed to get columns, got error %v", err)
	}

	hasLogID := false
	for _, column := range columns {
		if column.Name() == "log_id" {
			hasLogID = true
			autoIncrement, ok := column.AutoIncrement()
			if !ok || !autoIncrement {
				t.Fatalf("column log_id should be auto incrementment")
			}
		}
	}

	if !hasLogID {
		t.Fatalf("failed to found column log_id")
	}
}

        @Override
        public int getResponseCode() {
            return 200;
        }

        @Override
        public InputStream getInputStream() {
            return new ByteArrayInputStream("ok".getBytes());
        }

        @Override
        public Map<String, List<String>> getHeaderFields() {
            return Collections.emptyMap();
        }

        @Override

		}

		globalTrace.Publish(t)
	})
}

func httpTrace(f http.HandlerFunc, logBody bool) http.HandlerFunc {
	return func(w http.ResponseWriter, r *http.Request) {
		tc, ok := r.Context().Value(mcontext.ContextTraceKey).(*mcontext.TraceCtxt)
		if !ok {
			// Tracing is not enabled for this request
			f.ServeHTTP(w, r)
			return
		}

		tc.FuncName = getOpName(runtime.FuncForPC(reflect.ValueOf(f).Pointer()).Name())

func (listener *httpListener) Addr() (addr net.Addr) {
	addr = listener.listeners[0].Addr()
	if len(listener.listeners) == 1 {
		return addr
	}

	if tcpAddr, ok := addr.(*net.TCPAddr); ok {
		return &net.TCPAddr{
			IP:   net.IPv4zero,
			Port: tcpAddr.Port,
			Zone: tcpAddr.Zone,
		}
	}
	panic("unknown address type on listener")
}

                /*noutputs*/ 1,
                /*targets*/ nullptr, /*ntargets*/ 0,
                /*run_metadata*/ nullptr, status);
  if (VLOG_IS_ON(1) && status->status.ok()) {
    tensorflow::Tensor tensor;
    if (tensorflow::TF_TensorToTensor(ret, &tensor).ok()) {
      VLOG(1) << "Dequeued tensor content: " << tensor.DebugString();
    }
  }
  return ret;
}

    hero = session.get(Hero, hero_id)
    if not hero:
        raise HTTPException(status_code=404, detail="Hero not found")
    session.delete(hero)
    session.commit()

			t.Errorf("Test%d (%s): Invalid number of entries in targetsMap  got %d , want %d", i+1, test.name, len(dsc.targetsMap), len(test.expDsc.targetsMap))
		}
		for arn, tdsc := range dsc.targetsMap {
			expDsc, ok := test.expDsc.targetsMap[arn]
			if !ok || expDsc != tdsc {
				t.Errorf("Test%d (%s): Invalid  target replicate decision: got %+v, want %+v", i+1, test.name, tdsc, expDsc)
			}
		}
	}
}

var replicationStateTest = []struct {

		t.Fatalf("failed to get columns, got error %v", err)
	}

	hasLogID := false
	for _, column := range columns {
		if column.Name() == "log_id" {
			hasLogID = true
			autoIncrement, ok := column.AutoIncrement()
			if !ok || !autoIncrement {
				t.Fatalf("column log_id should be auto incrementment")
			}
		}
	}

	if !hasLogID {
		t.Fatalf("failed to found column log_id")
	}
}

                return Result.OK;
            } finally {
                reader.dispose();
            }
        }
        return Result.FAILED;
    }

    /**
     * Enumeration representing the possible results of thumbnail image processing.
     */
    protected enum Result {
        /** Thumbnail was successfully generated */
        OK,