// CHECK:         return %0 : tensor<1x?x4x128xf32>
func.func @real_dynamic_slice_strides_equal_to_1_signed(%arg0: tensor<1x?x4x256xf32>, %arg1: tensor<4xi32>, %arg2: tensor<4xi32>) -> tensor<1x?x4x128xf32> {
%cst = mhlo.constant dense<1> : tensor<4xi32>

  %2 = "tf.Conv2D"(%1, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<2x68x1x3xf32>, tensor<5x1x3x8xf32>) -> tensor<2x64x1x8xf32>
  %3 = "tf.Squeeze"(%2) {squeeze_dims = [-2]} : (tensor<2x64x1x8xf32>) -> tensor<2x64x8xf32>
  %4 = "tf.BatchToSpaceND"(%3, %cst_1, %cst) : (tensor<2x64x8xf32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x128x8xf32>
  func.return %4 : tensor<1x128x8xf32>

// CHECK-NOT: "tfl.batch_matmul"
func.func @Batchmatmul2FullyconnectedAdjx(%arg0: tensor<4x2x128xf32>) -> (tensor<4x128x1xf32>) {
  %0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32>
  %1 = "tfl.batch_matmul"(%arg0, %0) {adj_x = true, adj_y = false, asymmetric_quantize_inputs = false} : (tensor<4x2x128xf32>, tensor<2x1xf32>) -> tensor<4x128x1xf32>
  func.return %1 : tensor<4x128x1xf32>

// CHECK:      %[[R0:.*]] = "mhlo.concatenate"(%arg0, %arg2, %arg4, %arg6) <{dimension = 0 : i64}> : (tensor<1x72x128xf32>, tensor<1x72x128xf32>, tensor<1x72x128xf32>, tensor<1x72x128xf32>) -> tensor<4x72x128xf32>
// CHECK:      %[[R1:.*]] = "mhlo.concatenate"(%arg1, %arg3, %arg5, %arg7) <{dimension = 0 : i64}> : (tensor<1x128x72xf32>, tensor<1x128x72xf32>, tensor<1x128x72xf32>, tensor<1x128x72xf32>) -> tensor<4x128x72xf32>

      // CHECK-SAME:     scatter_dimension = 0
      //
      %1 = "tf.XlaReduceScatter"(%arg0, %cst_0, %cst) {reduce_op = "Add"} : (tensor<128x128xf32>, tensor<4x2xi32>, tensor<i32>) -> tensor<64x128xf32>
      func.return %1 : tensor<64x128xf32>
  }

  // CHECK-LABEL: func @tf_mod
  func.func @tf_mod(%arg1: tensor<2x2xf32>) -> tensor<2x2xf32> {
    %cst = "tf.Const"() {value = dense<7.000000e+00> : tensor<f32>} : () -> tensor<f32>

  // CHECK-SAME:   slice_sizes = dense<[1, 4, 128]>
  // CHECK-SAME: (tensor<2x4x128xf32>, tensor<2x1xi32>) -> tensor<2x4x128xf32>
  %0 =  "tf.GatherNd"(%arg0, %arg1) {Tindices = i32, Tparams = i32, device = ""} : (tensor<2x4x128xf32>, tensor<2x1xi32>) -> tensor<2x4x128xf32>
  func.return %0 : tensor<2x4x128xf32>
}

//===----------------------------------------------------------------------===//