consistent = false
				continue
			}
			ver := vers[0]
			if len(tops) == 0 {
				consistent = true
				topSig = ver.header
			} else {
				consistent = consistent && ver.header == topSig
			}
			tops = append(tops, vers[0])
		}

		// Check if done...
		if len(tops) < quorum {
			// We couldn't gather enough for quorum
			break
		}

    assertTrue(tokL.isSupertypeOf(new TypeToken<List<?>>() {}));

    TypeToken<Second> tokS = new TypeToken<Second>() {};
    assertTrue(tokS.isSupertypeOf(new TypeToken<Second>() {}));
    assertTrue(tokS.isSupertypeOf(new TypeToken<Third>() {}));
    assertTrue(tokS.isSupertypeOf(new TypeToken<Third<String, Integer>>() {}));

    TypeToken<List[]> tokA = new TypeToken<List[]>() {};

    assertTrue(tokL.isSupertypeOf(new TypeToken<List<?>>() {}));

    TypeToken<Second> tokS = new TypeToken<Second>() {};
    assertTrue(tokS.isSupertypeOf(new TypeToken<Second>() {}));
    assertTrue(tokS.isSupertypeOf(new TypeToken<Third>() {}));
    assertTrue(tokS.isSupertypeOf(new TypeToken<Third<String, Integer>>() {}));

    TypeToken<List[]> tokA = new TypeToken<List[]>() {};

      ASSERT_EQ(expected[i], data[i]) << "Different data at index " << i;
    }
  }

  std::vector<TF_Output> ToOutput(const std::vector<TF_Operation*> ops) {
    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,

    typedef tensorflow::protobuf::RepeatedPtrField<tensorflow::OpDef> OpDefs;
    const OpDefs& ops = op_list.op();
    bool found = std::find_if(ops.begin(), ops.end(),
                              [](const tensorflow::OpDef& op_def) {
                                return op_def.name() == "TestCApi";
                              }) != ops.end();
    EXPECT_TRUE(found);
    TF_DeleteBuffer(op_list_buffer);
  }
}

-nx????455ses--nx?5mzt5--nx?69vqhr--nx?7&8a4d5a4prebgm--nx?rb2c--nx??a!.&gro?jbo,mo&c?n??oc?ten??vd??b!.&0?1?2?3?4?5?6?7?8?9?a?b?c?d?e?f?g?h?i?j?k?l?m?n?o?p?q?r?s?t?u?v?w?x?y!srab,?z???c!b?za9a0cbgm--nx??e!.&ca?em!an??gro?ics?lim?moc?nue?ofni?t&en?rops??ude?v&og?t???a??g!.&gro?hsadtob,lenap:.nomead,,oc?saak,t&en?ikcats,???i&a?v??k!.&gro?lim?moc?su,ten?ude?vog?xx,??m!.&drp?gro?lim?mo&c?n??oc?ude?vog??pk??n!.&clp,dtl,eman?gro?hcs?i!bom??l&im?oc,?m&oc?rif,?neg,ogn,ten?ude?vog?zib:.&gl,ld,no,o&c,g,?...

  public static int frequency(Iterator<?> iterator, @Nullable Object element) {
    int count = 0;
    while (contains(iterator, element)) {
      // Since it lives in the same class, we know contains gets to the element and then stops,
      // though that isn't currently publicly documented.
      count++;
    }
    return count;
  }

  /**
   * Returns an iterator that cycles indefinitely over the elements of {@code iterable}.

        i = pathSegmentDelimiterOffset
        if (segmentHasTrailingSlash) i++
      }
    }

    /** Adds a path segment. If the input is ".." or equivalent, this pops a path segment. */
    private fun push(
      input: String,
      pos: Int,
      limit: Int,
      addTrailingSlash: Boolean,
      alreadyEncoded: Boolean,
    ) {
      val segment =

// --------------------------------------------------------------------------
// Load plugins containing custom ops and kernels

// TF_Library holds information about dynamically loaded TensorFlow plugins.
typedef struct TF_Library TF_Library;

// Load the library specified by library_filename and register the ops and
// kernels present in that library.
//

	}
	if !readData {
		for i := range v2bufs {
			metaDataPoolPut(v2bufs[i])
		}
	}

	// Return all the metadata.
	return metaFileInfos, errs
}

// Checking if an object is dangling costs some IOPS; hence implementing this function
// which decides which condition it is useful to check if an object is dangling
//
//	  errs: errors from reading xl.meta in all disks
//	   err: reduced errs