%0 = "tf.SparseToDense"(%arg0, %arg1, %arg2, %arg3) {validate_indices = true}: (tensor<3x5xi32>, tensor<3xi32>, tensor<2xf32>, tensor<f32>) -> tensor<?x?x?xf32>
  func.return %0 : tensor<?x?x?xf32>
  // CHECK-LABEL: sparse_to_dense_with_2d_sparse_indices_and_second_dim_greater_than_4

  // CHECK:      })
  // CHECK-SAME: (tensor<3x3xf32>, tensor<3x2xi32>, tensor<3xf32>) -> tensor<3x3xf32>

  // return %[[RESULT]] : tensor<3x3xf32>

    %cst = mhlo.constant dense<3> : tensor<2xi32>
    %0 = "tf.SparseToDense"(%arg0, %cst, %arg1, %arg2) {validate_indices = true}: (tensor<3x2xi32>, tensor<2xi32>, tensor<3xf32>, tensor<f32>) -> tensor<3x3xf32>
    func.return %0 : tensor<3x3xf32>
  }

  // CHECK-LABEL: reverse_sequence

      "SegmentMaxV2",
      "SelfAdjointEigV2",
      "SoftmaxCrossEntropyWithLogits",
      "SpaceToBatch",
      "SpaceToBatchND",
      "SpaceToDepth",
      "SparseMatMul",
      "SparseToDense",
      "StackCloseV2",
      "StackPopV2",
      "StackPushV2",
      "StackV2",
      "StatefulPartitionedCall",
      "StatefulStandardNormalV2",
      "StatefulTruncatedNormal",

    *   ResourceVariable support for `gather_nd`.
    *   `ResourceVariable` and `Variable` no longer accepts `constraint` in the
        constructor, nor expose it as a @property.
    *   Added gradient for `SparseToDense` op.
    *   Expose a flag that allows the number of threads to vary across Python
        benchmarks.
    *   `image.resize` in 2.0 now supports gradients for the new resize kernels.

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<1>;
  TF_DerivedOperandTypeAttr Tindices = TF_DerivedOperandTypeAttr<0>;
}

def TF_SparseToDenseOp : TF_Op<"SparseToDense", [Pure]> {
  let summary = "Converts a sparse representation into a dense tensor.";

  let description = [{
Builds an array `dense` with shape `output_shape` such that

```