// Parses "input_sharding_configuration" attribute and returns a list where i-th
// element is a list of mlir::Value's which represent inputs for the TPU
// computation corresponding to i-th logical device. If the attribute does not
// exist, the all inputs are placed on logical core 0.
mlir::LogicalResult ExtractInputsForLogicalDevices(
    int num_cores_per_replica, mlir::tf_device::ClusterFuncOp cluster_func,
    mlir::OpBuilder* builder,

	op_ALGF    uint32 = 0xE31A // FORMAT_RXY1       ADD LOGICAL (64<-32)
	op_ALGFI   uint32 = 0xC20A // FORMAT_RIL1       ADD LOGICAL IMMEDIATE (64<-32)
	op_ALGFR   uint32 = 0xB91A // FORMAT_RRE        ADD LOGICAL (64<-32)
	op_ALGHSIK uint32 = 0xECDB // FORMAT_RIE4       ADD LOGICAL WITH SIGNED IMMEDIATE (64<-16)
	op_ALGR    uint32 = 0xB90A // FORMAT_RRE        ADD LOGICAL (64)

  // logical core by default.
  if (!sharding_attrs) {
    (*input_list)[0] = cluster_func_inputs;
    return mlir::success();
  }

  // Enumerate sharding configuration for each inputs. If input has replicate
  // sharding then all logical devices take the value as input. If input has
  // maximal sharding then only the specified logical device take the value as
  // the input.

    llvm::ArrayRef<int64_t> device_assignment_attr);

// Virtual device name of the passed logical core. The logical core is the index
// of a core within a replica.
std::string GetDeviceAliasForLogicalCore(int core_index);

// Virtual device name of the host that is associated with the passed logical
// core. The logical core is the index of a core within a replica.
std::string GetDeviceAliasForHostOfLogicalCore(int core_index);

// pcBuf and dst should not overlap.
//
// fpunwindExpand checks if pcBuf contains logical frames (which include inlined
// frames) or physical frames (produced by frame pointer unwinding) using a
// sentinel value in pcBuf[0]. Logical frames are simply returned without the
// sentinel. Physical frames are turned into logical frames via inline unwinding
// and by applying the skip value that's stored in pcBuf[0].

		a.ObjectSizeGreaterThan == 0 && a.ObjectSizeLessThan == 0
}

// Validate - validates the And field
func (a And) Validate() error {
	// > This is used in a Lifecycle Rule Filter to apply a logical AND to two or more predicates.
	// ref: https://docs.aws.amazon.com/AmazonS3/latest/API/API_LifecycleRuleAndOperator.html
	// i.e, predCount >= 2
	var predCount int
	if a.Prefix.set {
		predCount++
	}

def InputLoweringMetricsPass : Pass<"input-lowering-metrics-pass", "mlir::func::FuncOp"> {

  let summary = "Collects various metrics about the input to the lowering "
                "portion of the bridge. This is a logical no-op.";

  let description = [{
    Gathers metrics about the input MLIR to Phase 2 of the TFXLA Bridge, which
    does a strict semantic lowering from Tensorflow ops to XLA HLO.
  }];

 */
@ServiceScope(Scope.BuildTree.class)
public interface ProblemDiagnosticsFactory {
    /**
     * Creates a new stream of problems. Each problem stream produces diagnostics for some logical set of problems, applying limits to
     * the number of stack traces captured. Each stream has its own limits.
     */
    ProblemStream newStream();

    /**
     * Creates a new stream of problems without any limits.

        Writer destination = new StringWriter()

        when:
        generator.generateScript(details, destination)

        then:
        destination.toString().contains(/set DEFAULT_JVM_OPTS="-Dfoo=b\"ar baz" "-Xi\"\"nt" "-Xpatho\\\"logical"/)
    }

      old_parallel_execute, new_parallel_execute);

  // Extract inputs for each block of the parallel_execute op. The i-th
  // element in the list represents the input lists to TPU computation for
  // i-th logical core.
  llvm::SmallVector<llvm::SmallVector<mlir::Value, 4>, 4> input_list;
  builder->setInsertionPoint(*new_parallel_execute);
  auto result = tensorflow::ExtractInputsForLogicalDevices(