ConcatV2 1
 Const 30
 ExpandDims 3
 Less 1
 Mean 2
 Minimum 1
 Mul 3
 Pack 2
 Pad 1
 Range 1
 RealDiv 1
 Reshape 8
 Shape 7
 Size 1
 Slice 2
 Snapshot 5
 SparseSoftmaxCrossEntropyWithLogits 1
 StridedSlice 2
 Sub 3
 Sum 1
 Tile 1
 Transpose 2
 Unpack 1
cluster 2 size 44
 Cast 2
 ConcatV2 2
 Const 18
 ExpandDims 1
 GatherV2 2
 Less 1
 MatMul 1

    %19 = "tf.Reshape"(%18, %7) : (tensor<2x1001xf32>, tensor<2xi32>) -> tensor<2x1001xf32>
    %loss, %backprop = "tf.SparseSoftmaxCrossEntropyWithLogits"(%19, %13) : (tensor<2x1001xf32>, tensor<2xi64>) -> (tensor<2xf32>, tensor<2x1001xf32>)
    %20 = "tf.Sum"(%loss, %6) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>

}

// -----

//===--------------------------------------------------------------------===//
//  tf.SparseSoftmaxCrossEntropyWithLogits
//===--------------------------------------------------------------------===//

// Test valid tf.SparseSoftmaxCrossEntropyWithLogits
// CHECK-LABEL: func @testSparseSoftmaxCrossEntropyWithLogits

          // clang-format on
          {"PWRED",
           {"ArgMax", "ArgMin", "DiagPart", "Softmax",
            "SparseSoftmaxCrossEntropyWithLogits", "LogSoftmax"}},
          {"REDUCEWINDOW",
           {"ArgMax", "ArgMin", "DiagPart", "Softmax",
            "SparseSoftmaxCrossEntropyWithLogits", "LogSoftmax"}},
          {"REDUCEWINDOWPW", {"BiasAddGrad", "LRN", "LRNGrad"}},
          {"BN",

FunctionsAcceptingNoneForIndicesMap() {
  static auto* const m =
      new std::unordered_map<string, std::unordered_set<int>>({
          {"SoftmaxCrossEntropyWithLogits", {1}},
          {"SparseSoftmaxCrossEntropyWithLogits", {1}},
          {"FusedBatchNorm", {1, 2, 3, 4}},
      });
  return m;
}

}  // namespace

// If over kMinAggregateCount gradients are accumulated and the total

  op: "Mul"
  input: "training/LossScaleOptimizer/gradients/loss_1/activation_49_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/ExpandDims"
  input: "loss_1/activation_49_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits:1"

}
node {
  name: "training/SGD/gradients/loss_1/activation_49_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/mul"
  op: "Mul"
  input: "loss_1/activation_49_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits:1"
  input: "training/SGD/gradients/loss_1/activation_49_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits_grad/ExpandDims"

  TF_DerivedOperandTypeAttr Tidx = TF_DerivedOperandTypeAttr<1>;
  TF_DerivedOperandTypeAttr Tsegmentids = TF_DerivedOperandTypeAttr<2>;
}

def TF_SparseSoftmaxCrossEntropyWithLogitsOp : TF_Op<"SparseSoftmaxCrossEntropyWithLogits", [Pure]> {
  let summary = [{
Computes softmax cross entropy cost and gradients to backpropagate.
  }];

  let description = [{