case C_SOREG:
		return cmp(C_ZOREG, b)

	case C_LOREG:
		return cmp(C_SOREG, b)

	case C_XOREG:
		return cmp(C_REG, b) || cmp(C_ZOREG, b)

	// An even/odd register input always matches the regular register types.
	case C_REG:
		return cmp(C_REGP, b) || (b == C_ZCON && r0iszero != 0)
	case C_FREG:
		return cmp(C_FREGP, b)
	case C_VSREG:
		/* Allow any VR argument as a VSR operand. */

   * the original {@code ClosingFuture} instance.
   *
   * @param exceptionType the exception type that triggers use of {@code fallback}. The exception
   *     type is matched against this step's exception. "This step's exception" means the cause of
   *     the {@link ExecutionException} thrown by {@link Future#get()} on the {@link Future}

    TFL_OperandHasAtleastRank<0, 2>,
    PredOpTrait<"input and result must have the same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    Pure]> {
  let summary = [{
    Returns a batched matrix tensor with new batched diagonal values.
  }];

  let description = [{
Given `input` and `diagonal`, this operation returns a tensor with the
same shape and values as `input`, except for the main diagonal of the

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *

   * of reads may not be immediately reflected on the algorithm's data structures. These structures
   * are guarded by a lock and operations are applied in batches to avoid lock contention. The
   * penalty of applying the batches is spread across threads so that the amortized cost is slightly
   * higher than performing just the operation without enforcing the capacity constraint.
   *

        for (def line : stream.call().readLines()) {
            def matcher = pattern.matcher(line)
            if (matcher.matches()) {
                dirs.add(new TestFile(matcher.group(1)))
            }
        }
        return dirs
    }

    TestFile getGcFile() {
        return cacheDir.file("gc.properties")

{return s[e](t)}catch(t){return null}finally{a&&a.forEach(function(t){return t()})}}var Tt=/(^|[^\\],)\s*[!>+~-]/,Et=/([!>+~-])(?=\s+[!>+~-]|\s*$)/g;function At(t){return D(t)&&t.match(Tt)}var _t=/.*?[^\\](?:,|$)/g;var Ct=Element.prototype,Nt=Ct.matches||Ct.webkitMatchesSelector||Ct.msMatchesSelector;function Mt(t,e){return V(t).some(function(t){return Nt.call(t,e)})}var zt=Ct.closest||function(t){var e=this;do{if(Mt(e,t))return e}while(e=Pt(e))};function Dt(t,e){return w(e,">")&&(e=e.slice(1)),...

  NodeDef arg_def;
  TF_RETURN_IF_ERROR(arg_builder.Finalize(&arg_def));

  TF_ASSIGN_OR_RETURN(Node * arg_node, function_body.graph->AddNode(arg_def));
  return arg_node;
}

// Add _Retval node that matches newly added `arg_node` and connect `arg_node`
// to it.
Status AddMatchingRetvalNode(const FunctionBody& function_body,
                             const int arg_idx, const DataType& data_type,

  rewriter.replaceAllUsesWith(uniform_quantize_op->getResult(0),
                              op->getResult(0));
  return op->getResult(0).getType().cast<TensorType>().clone(
      new_result_quantized_type);
}

// Matches kernel dimension numbers, ranks of input and output and constant
// kernel for legalization to TFLite convolution ops.
LogicalResult MatchConvolutionFormat(stablehlo::ConvolutionOp op) {

								Status:             v1.ConditionTrue,
								LastTransitionTime: *tc.lastScaleTime,
								Reason:             "ReadyForNewScale",
								Message:            "recommended size matches current size",
							},
							{
								Type:               autoscalingv2.ScalingActive,
								Status:             v1.ConditionTrue,
								LastTransitionTime: *tc.lastScaleTime,