// CHECK:           [[LHSSHAPEEXTENTS:%.*]] = shape.to_extent_tensor [[LHSBCASTSHAPE]]
// CHECK:           [[LHSBCAST:%.*]] = "mhlo.dynamic_broadcast_in_dim"([[LHS]], [[LHSSHAPEEXTENTS]]) <{broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>}> : (tensor<1x4x2xf32>, tensor<3xindex>) -> tensor<3x4x2xf32>
// CHECK:           [[RHSBCASTSHAPE:%.*]] = shape.concat [[BCASTHEAD]], [[RHSTAIL]]
// CHECK:           [[RESULT:%.*]] = "mhlo.dot_general"([[LHSBCAST]], [[RHS]])

  %begin0 = arith.constant dense<0> : tensor<4xi64>
  %shape0 = arith.constant dense<[2,3,4,4]> : tensor<4xi64>
  %begin1 = arith.constant dense<1> : tensor<4xi64>
  %shape1 = arith.constant dense<[1,2,3,4]> : tensor<4xi64>
  %0 = "tfl.slice"(%arg0, %begin0, %shape0) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<2x3x4x4xf32>

func.func @Int64SliceBeginSize(%arg0: tensor<4x128x32xf32>) -> tensor<1x128x32xf32> {
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3xi64>} : () -> tensor<3xi64>
  %1 = "tfl.pseudo_const"() {value = dense<[1, 128, 32]> : tensor<3xi64>} : () -> tensor<3xi64>
  %2 = "tfl.slice"(%arg0, %0, %1) : (tensor<4x128x32xf32>, tensor<3xi64>, tensor<3xi64>) -> tensor<1x128x32xf32>
  func.return %2 : tensor<1x128x32xf32>

//        limit_indices = dense<[4, 1]> : tensor<2xi64>,
//        start_indices = dense<0> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x1xf32>
// %1 = "mhlo.slice"(%input) {
//        limit_indices = dense<[4, 3]> : tensor<2xi64>,
//        start_indices = dense<[0, 1]> : tensor<2xi64>,
//        strides = dense<1> : tensor<2xi64>} :
//        (tensor<4x6xf32>) -> tensor<4x2xf32>

    SmallVector<Value> start_indices(rank);
    for (auto [i, start_index] : llvm::enumerate(op.getStartIndices())) {
      // Start indices should be casted from tensor<i64> to tensor<1xi64>.
      auto cast = rewriter.create<TFL::BitcastOp>(
          op->getLoc(), single_element_type, start_index);
      int64_t upper_limit_idx = operand_shape[i] - slice_sizes[i];
      auto upper_limit_attr =

}

// Decompose tf.RngReadAndSkip.
//
// For Philox, the resource variable holds a tensor<3xi64> with the state:
//   [counter_lo, counter_hi, key]
//
//   RngReadAndSkip increments the 128 bit counter value by 256 * delta and
//   returns the original state value.
//
// For Threefry, the resource variable holds a tensor<2xi64> with the state:
//   [counter, key]
//

    // CHECK: %[[CONST3:.*]] = "tf.Const"() <{value = dense<
    // CHECK-SAME: [7, 7, 3, 64]
    // CHECK: %[[CONST4:.*]] = "tf.Const"() <{value = dense<
    // CHECK-SAME: 0
    // CHECK: %[[SLICE0:.*]] = "tf.Slice"(%[[RESHAPE1:.*]], %[[CONST4:.*]], %[[CONST3:.*]]) : (tensor<8x8x3x64xf32>, tensor<4xi64>, tensor<4xi32>) -> tensor<7x7x3x64xf32>

  func.return %3 : tensor<2xi64>
  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[1, 2]> : tensor<2xi64>}> : () -> tensor<2xi64>
  // CHECK: return %[[CST]]
}

// CHECK-LABEL: testFoldStridedSliceShapeDynamicOutput
func.func @testFoldStridedSliceShapeDynamicOutput(%arg0: tensor<?x1x2x?xf32>) -> (tensor<?xi32>) {

  %2 = mhlo.constant dense<0.000000e+00> : tensor<f32>

    -> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>, tensor<?xi64>, tensor<2xi64>, tensor<2xi64>, tensor<32xf32>, tensor<32xf32>)

  // CHECK: tfrt.return {{.*}}, [[outputs]]#0
  func.return %outputs#0 : tensor<?x2xi64>
}

// CHECK-LABEL: func @no_native