functionally equivalent to the function referenced by `tf_device.cluster_func`.
    This makes the module to be jit-compiled and executed on TPU.
    If it is not possible to rewrite the operation or device assignment fails,
    a failure will be returned.

    Note, many parameters to the `tf_device.cluster_func` are omitted in this
    and following examples.
    For example, a non replicated `tf_device.cluster_func`:

  return topology;
}

// Determine execution devices when topology and device assignment are defined.
// With a topology device coordinate to task and device mapping, device
// assignment device coordinates can then be mapped to task and device for TPU
// devices. The device assignment array is also validated.
//
// A valid device assignment array must have:
//  - device coordinates within the topology mesh shape

CreateConvertReadonlyReferenceVariablesToResourceVariablesPass();

// Creates a simple device assignment pass on TF dialect for CoreRT use case.
std::unique_ptr<OperationPass<func::FuncOp>> CreateSimpleTFDeviceAssignmentPass(
    llvm::StringRef default_device = "cpu");

// Creates a pass to perform device assignment for TF dialect ops that do not
// have device assignment, by using the device attribute of the function.

// RUN: tf-opt %s -tf-layout-assignment=force-data-format=NHWC -verify-diagnostics | FileCheck %s --dump-input=always

// IMPORTANT: Tensor shapes do not match convolution parameters (stride,
// dilations, etc...). This test only verifies that changing convolution data
// layout will update all the attributes.

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> {

//
// Note: [Eval] and CheckExpr should not be used instead of running Check
// to compute types and values, but in addition to Check, as these
// functions ignore the context in which an expression is used (e.g., an
// assignment). Thus, top-level untyped constants will return an
// untyped type rather than the respective context-specific type.
func CheckExpr(fset *token.FileSet, pkg *Package, pos token.Pos, expr ast.Expr, info *Info) (err error) {

  if (init_func_op) {
    if (failed(
            applyPatternsAndFoldGreedily(init_func_op, std::move(patterns)))) {
      init_func_op->emitError(
          "Failed to remove variable assignment by const patterns.");
      signalPassFailure();
    }
  } else {
    LOG(INFO) << "Initializer function with type 'restore_op' does not exist. "

// as2func orders OAS2FUNC nodes. It creates temporaries to ensure left-to-right assignment.
// The caller should order the right-hand side of the assignment before calling order.as2func.
// It rewrites,
//
//	a, b, a = ...
//
// as
//
//	tmp1, tmp2, tmp3 = ...
//	a, b, a = tmp1, tmp2, tmp3
//
// This is necessary to ensure left to right assignment order.
func (o *orderState) as2func(n *ir.AssignListStmt) {

	}
}

// SetMemoryAssignments sets ContainerMemoryAssignments by using the passed parameter
func (sc *stateCheckpoint) SetMemoryAssignments(assignments ContainerMemoryAssignments) {
	sc.Lock()
	defer sc.Unlock()

	sc.cache.SetMemoryAssignments(assignments)
	err := sc.storeState()
	if err != nil {
		klog.InfoS("Store state to checkpoint error", "err", err)
	}
}

	}
	return &unifier{handles, 0, enableInterfaceInference}
}

// unifyMode controls the behavior of the unifier.
type unifyMode uint

const (
	// If assign is set, we are unifying types involved in an assignment:
	// they may match inexactly at the top, but element types must match
	// exactly.
	assign unifyMode = 1 << iota

	// If exact is set, types unify if they are identical (or can be

        lenient_issubclass as lenient_issubclass,  # noqa: F401
    )

    GetJsonSchemaHandler = Any  # type: ignore[assignment,misc]
    JsonSchemaValue = Dict[str, Any]  # type: ignore[misc]
    CoreSchema = Any  # type: ignore[assignment,misc]

    sequence_shapes = {
        SHAPE_LIST,
        SHAPE_SET,
        SHAPE_FROZENSET,
        SHAPE_TUPLE,
        SHAPE_SEQUENCE,