// Handle the case where we have no malloc header.
		scanSize = span.writeHeapBitsSmall(x, dataSize, typ)
	} else {
		if typ.Kind_&abi.KindGCProg != 0 {
			// Allocate space to unroll the gcprog. This space will consist of
			// a dummy _type value and the unrolled gcprog. The dummy _type will
			// refer to the bitmap, and the mspan will refer to the dummy _type.
			if span.spanclass.sizeclass() != 0 {
				throw("GCProg for type that isn't large")

//   %1 = mhlo.reshape %param : (1xCxZ) -> CxZ
//   mhlo.dot_general %input, %1 {batch_dims = []}
// To:
//   mhlo.dot_general %input, %param {batch_dims = [0]}
//
// This usage will mostly come from tf-unroll-batch-matmul, so it's fine to only
// handle the case where batching dim is the leftmost dim.
LogicalResult ConvertReshapeDotRhsToBatchedDot(mhlo::DotGeneralOp dot,

std::unique_ptr<OperationPass<func::FuncOp>> CreateLayoutAssignmentPass();

// Guarantee that all FuncOp's have a single use.
std::unique_ptr<OperationPass<ModuleOp>> CreateGuaranteeAllFuncsOneUsePass();

// Optional pass which will unroll BatchMatMul and use only MatMul
std::unique_ptr<OperationPass<func::FuncOp>> CreateUnrollBatchMatMulPassPass();

// Optional pass which will map TF BatchMatMul to TF Einsum

    ```

    The pass also works across control flow and functional calls.
  }];
}



def UnrollBatchMatMulPass : Pass<"tf-unroll-batch-matmul", "mlir::func::FuncOp"> {
  let summary = "Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops.";
  let constructor = "TF::CreateUnrollBatchMatMulPassPass()";
}

See the License for the specific language governing permissions and
limitations under the License.
*/

package schema

// Unfold expands vendor extensions of a structural schema.
// It mutates the receiver.
func (s *Structural) Unfold() *Structural {
	if s == nil {
		return nil
	}

	mapper := Visitor{
		Structural: func(s *Structural) bool {
			if !s.XIntOrString {
				return false

// RUN: tf-opt %s -unfold-large-splat-constant | FileCheck %s

// CHECK-LABEL: @unfold_large_constant_splat
func.func @unfold_large_constant_splat() -> (tensor<10x10xf32>, tensor<1000x1000xf32>) {
  %0 = arith.constant dense<0.00000e+00> : tensor<10x10xf32>
  %1 = arith.constant dense<1.00000e+00> : tensor<1000x1000xf32>
  func.return %0, %1 : tensor<10x10xf32>, tensor<1000x1000xf32>

  // CHECK-DAG: %cst = arith.constant dense<0.000000e+00> : tensor<10x10xf32>

  tf_device.replicate ([%var0, %var1] as %rvar,
                       [%state_var0, %state_var1] as %rstate) {
    tf.TPUReshardVariablesOp(%rvar, %default_format, %rstate)
  }
```
### `-tf-unroll-batch-matmul`

_Unroll TF BatchMatMul op into Reshape, Slice, MatMul, Pack ops._

### `-tf-verify-for-export`

_Verify module is suitable for export back to TF Graph_

apiVersion: release-notes/v2
kind: feature
area: istioctl
issue:
  - 50248
releaseNotes:
- |

            .assertTestPassed("$testMethod [param: value1, #0]")
            .assertTestPassed("$testMethod [param: value2, #1]")

        where:
        testMethod << ["sub unrolled test", "super unrolled test", "super super unrolled test"]
    }

    def "can filter unrolled tests with parameter name in test header"() {
        when:
        succeeds("test", "--tests", "SubClass.$testMethod param=#param")

        then:

apiVersion: release-notes/v2
kind: feature
area: traffic-management
issue:
- 50355
releaseNotes:
- |