}

// -----

func.func @unsorted_segment_min(%arg0: tensor<8xf32>, %arg1: tensor<8xi32>) -> tensor<8xf32> {
  %num_segments = "tf.Const"() {value = dense<8> : tensor<i32>} : () -> tensor<i32>
  %0 = "tf.UnsortedSegmentMin"(%arg0, %arg1, %num_segments) : (tensor<8xf32>, tensor<8xi32>, tensor<i32>) -> tensor<8xf32>
  func.return %0 : tensor<8xf32>
  // CHECK-LABEL: unsorted_segment_min

// CHECK-LABEL: unsortedSegmentReduction
func.func @unsortedSegmentReduction(%data: tensor<?x10x8xf32>, %segment_ids: tensor<7x?xi32>, %num_segments: tensor<i32>) {
  // CHECK: tf.UnsortedSegmentMin
  %0 = "tf.UnsortedSegmentMin"(%data, %segment_ids, %num_segments) : (tensor<?x10x8xf32>, tensor<7x?xi32>, tensor<i32>) -> (tensor<?x8xf32>)
  func.return
}

// -----

}

def TFL_UnsortedSegmentMinOp: TFL_Op<"unsorted_segment_min", [
    Pure, PredOpTrait<"input and output must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>]> {

  let summary = "UnsortedSegmentMin operator";

  let description = [{
    Computes the minimum value along segments of a tensor such that
    output[i] = min(data[j....]) where segment_ids[j...] = i