"SegmentSumV2",
      "SegmentProdV2",
      "SegmentMinV2",
      "SegmentMaxV2",
      "SelfAdjointEigV2",
      "SoftmaxCrossEntropyWithLogits",
      "SpaceToBatch",
      "SpaceToBatchND",
      "SpaceToDepth",
      "SparseMatMul",
      "SparseToDense",
      "StackCloseV2",
      "StackPopV2",
      "StackPushV2",
      "StackV2",
      "StatefulPartitionedCall",

    %1 = "tf.Const"() {value = dense<2> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
    %2 = "tf.Identity"(%1) {device = ""} : (tensor<2x2xi32>) -> tensor<2x2xi32>
    // CHECK: SpaceToBatchND{{.*}}-> tensor<4x98x130x128xf32>
    %3 = "tf.SpaceToBatchND"(%arg0, %0, %2) {device = ""} : (tensor<1x192x256x128xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x?x?x128xf32>
    func.return
  }

  // CHECK-LABEL: check_subtyperefinement

*   Fixes a missing validation which results in undefined behavior in `QuantizedConv2D` ([CVE-2022-29201](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-29201))
*   Fixes an integer overflow in `SpaceToBatchND` ([CVE-2022-29203](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-29203))

    SameOperandsAndResultsScale,
    TFL_OperandHasRankRange<0, 3, 4>,
    PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>
  ]> {
  let summary = "SpaceToBatchNd operator";

  let description = [{
    This operation reshapes space dimensions into the "batch" dimension 0
  }];

  let arguments = (ins

  }];
}

def TF_SpaceToBatchOp : TF_Op<"SpaceToBatch", [Pure]> {
  let summary = "SpaceToBatch for 4-D tensors of type T.";

  let description = [{
This is a legacy version of the more general SpaceToBatchND.

Zero-pads and then rearranges (permutes) blocks of spatial data into batch.
More specifically, this op outputs a copy of the input tensor where values from