}
	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

# A shared object which includes registration mechanisms for ops and
# kernels. Does not include the implementations of any ops or kernels. Instead,
# the library which loads libtensorflow_framework.so
# (e.g. _pywrap_tensorflow_internal.so for Python, libtensorflow.so for the C
# API) is responsible for registering ops with libtensorflow_framework.so. In
# addition to this core set of ops, user libraries which are loaded (via

				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
		}

build:macos_arm64 --platforms=@build_bazel_apple_support//configs/platforms:darwin_arm64

# iOS configs for each architecture and the fat binary builds.
build:ios --apple_platform_type=ios
build:ios --copt=-fembed-bitcode
build:ios --copt=-Wno-c++11-narrowing
build:ios --config=clang_local
build:ios --config=apple-toolchain
build:ios_armv7 --config=ios
build:ios_armv7 --cpu=ios_armv7

      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,

// --------------------------------------------------------------------------
// 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.
//

-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,?...

					continue
				}

				// if domain IPs intersect then it won't be an empty set.
				// such an intersection means that bucket exists on etcd.
				// but if we do see a difference with local domain IPs with
				// hostSlice from etcd then we should update with newer
				// domainIPs, we proceed to do that here.
				bucketsToBeUpdated.Add(bucket)
				continue
			}

    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);
  }
}

	VREV32	V5.B16, V5.B16                  // a508206e
	VREV64	V2.S2, V3.S2                    // 4308a00e
	VREV64	V2.S4, V3.S4                    // 4308a04e

// logical ops
//
// make sure constants get encoded into an instruction when it could
	AND	R1@>33, R2
	AND	$(1<<63), R1                        // AND	$-9223372036854775808, R1       // 21004192