// CHECK:   mhlo.return [[CMP]]
  // CHECK: } do {
  // CHECK:   [[SRC_IDX:%.*]] = "mhlo.dynamic_slice"([[ITER_ARG2]], [[ITER_ARG0]]) <{slice_sizes = dense<1> : tensor<1xi64>}> : (tensor<4xi32>, tensor<i32>) -> tensor<1xi32>
  // CHECK:   [[SWP_IDX:%.*]] = "mhlo.dynamic_slice"([[ITER_ARG1]], [[ITER_ARG0]]) <{slice_sizes = dense<1> : tensor<1xi64>}> : (tensor<4xi32>, tensor<i32>) -> tensor<1xi32>

// CHECK:           return %[[VAL_14]] : tensor<4x2xf32>
// CHECK:         }
func.func @convert_dynamic_slice(%arg0: tensor<7x3xf32>, %arg1: tensor<i32>, %arg2: tensor<i32>) -> tensor<4x2xf32> {
  %0 = "mhlo.dynamic_slice"(%arg0, %arg1, %arg2) <{slice_sizes = dense<[4, 2]> : tensor<2xi64>}> : (tensor<7x3xf32>, tensor<i32>, tensor<i32>) -> tensor<4x2xf32>
  func.return %0 : tensor<4x2xf32>
}

// indices randomly generated via HLO rng_uniform ops. Otherwise, it is
// translated into an HLO while op to first emulate shuffling indices using
// HLO dynamic_slice and dynamic_update_slice ops, then finally HLO gather
// with the shuffled indices.
class ConvertRandomShuffleOp : public OpRewritePattern<TF::RandomShuffleOp> {
 public:
  using OpRewritePattern::OpRewritePattern;