}

 private:
  string& str_;
};

TEST(Dump, TextualIrToFileSuccess) {
  Graph graph(OpRegistry::Global());
  Node* node;
  TF_CHECK_OK(NodeBuilder("A", "NoOp").Finalize(&graph, &node));

  setenv("TF_DUMP_GRAPH_PREFIX", testing::TmpDir().c_str(), 1);
  UseMlirForGraphDump(MlirDumpConfig());
  string ret = DumpGraphToFile("tir", graph);

      %zero = arith.constant dense<0> : tensor<i32>
      %ne = "tf.NotEqual"(%carg1, %zero) : (tensor<i32>, tensor<i32>) -> tensor<i1>
      "tf.Yield"(%ne) : (tensor<i1>) -> ()
    },
    {
      // loop body
      ^bb0(%barg0: tensor<*xf32>, %barg1: tensor<i32>):
      %add = "tf.Add"(%barg0, %barg0) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
      %one = arith.constant dense<1> : tensor<i32>

        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

      func.func private @function_2() -> () attributes {tf._original_func_name = "__func_2"} {
        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

  workflow_dispatch:

permissions: {}

jobs:
  feedback:
    permissions:
      issues: write
      pull-requests: write
    runs-on: ubuntu-latest
    steps:
      # Feedback loop: ask for something on PR/Issue and close if not provided or return to the queue on update.
      # https://github.com/gradle/issue-management-action/blob/main/src/feedback.ts
      - uses: gradle/issue-management-action@v1

  // Collect external values used.
  llvm::SetVector<Value> extern_values;

  // The basic block arguments correspond to values that are loop carried, while
  // all those post are loop independent. Initialize extern_values with while_op
  // not loop carried operands.
  auto num_loop_carried = while_op.getCond().getNumArguments();
  auto not_carried_operands =
      while_op.getOperands().drop_front(num_loop_carried);

// Test to verify loop outlining.

// RUN: tf-opt --split-input-file --tfl-while-loop-outline %s | FileCheck %s
// Check that while loop outlining is nop if re-ran.
// RUN: tf-opt --tfl-while-loop-outline %s -o %t1
// RUN: tf-opt --tfl-while-loop-outline %t1 -o %t2
// RUN: diff %t1 %t2

// CHECK-LABEL: func @while
func.func @while() -> tensor<1xf32>
    attributes {tf.entry_function = {outputs = "result"}} {

	// Eliminate bounds checks in the loop.
	size := len(x.limbs)
	xLimbs := x.limbs[:size]
	yLimbs := y.limbs[:size]

	equal := yes
	for i := 0; i < size; i++ {
		equal &= ctEq(xLimbs[i], yLimbs[i])
	}
	return equal
}

// IsZero returns 1 if x == 0, and 0 otherwise.
func (x *Nat) IsZero() choice {
	// Eliminate bounds checks in the loop.
	size := len(x.limbs)
	xLimbs := x.limbs[:size]

		}
	}
	m.cache.RUnlock()

	// Loop through all inactive pods and call UnprepareResources on them.
	for _, podClaims := range inactivePodClaims {
		if err := m.unprepareResources(podClaims.uid, podClaims.namespace, podClaims.claimNames); err != nil {
			klog.ErrorS(err, "Unpreparing pod resources in reconcile loop", "podUID", podClaims.uid)
		}
	}
}

// Analyzes while loops to compute resourceIDs for the loop results.
//
// (1) The base case for the analysis is that if the loop body does not execute
//     at all, the resource IDs for each result is the same as the resource IDs
//     of the corresponding input.
// (2) If the loop does execute one or more times, then we need to account for
//     data flow through the body of the while loop. If result #r is the same

            result.setAvailable(true);
        }

        return result;
    }

    public void add(RepositorySystemSession session, LocalArtifactRegistration request) {
        // noop
    }

    public LocalMetadataResult find(RepositorySystemSession session, LocalMetadataRequest request) {
        LocalMetadataResult result = new LocalMetadataResult(request);

        String path;