%cst = "tf.Const"() { value = dense<[10, 3, 2]> : tensor<3xi32> } : () -> tensor<3xi32>
  %1 = "tf.ScatterNd"(%arg0, %arg1, %cst) : (tensor<5x1xi32>, tensor<5x3x2xf32>, tensor<3xi32>) -> tensor<10x3x2xf32>
  func.return %1 : tensor<10x3x2xf32>

// CHECK-LABEL:scatterNdVectorIndices
// CHECK: %[[CST:.*]] = arith.constant dense<[10, 3, 2]> : tensor<3xi32>

// -----

// Tests that a float `stablehlo.transpose` is not converted to `tfl.transpose`.

func.func @transpose_float(%arg0: tensor<2x3x4xf32>) -> tensor<4x3x2xf32> {
  %0 = stablehlo.transpose %arg0, dims = [2, 1, 0] : (tensor<2x3x4xf32>) -> tensor<4x3x2xf32>
  return %0 : tensor<4x3x2xf32>
}
// CHECK-LABEL: transpose_float
// CHECK-NOT: tfl.transpose
// CHECK: stablehlo.transpose

// -----

      %2 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x8x2xi32>) -> tensor<1x8x2xf32>
      tf_device.return %2 : tensor<1x8x2xf32>
    // CHECK: () -> tensor<1x8x2xf32>
    }) {device = "/device:CPU:0"} : () -> tensor<*xf32>
    // CHECK: "tf.Cast"(%{{.*}}) <{Truncate = false}> : (tensor<1x8x2xf32>) -> tensor<*xf32>
    // CHECK: (tensor<i32>, tensor<1x8x2xf32>) -> (tensor<1x8x1xf32>, tensor<1x8x1xf32>)

  %push = "tf.XlaDynamicUpdateSlice"(%tl, %arg0_reshaped, %start_indices) : (tensor<10x8x4xf32>, tensor<1x8x4xf32>, tensor<3xi32>) -> tensor<10x8x4xf32>
  %one = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %next_index_in_list = "tf.AddV2"(%index_in_list, %one) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  return

}

// -----

// Mismatching batch dimensions.
func.func @testBatchMatMul(%lhs: tensor<1x3x5xf32>, %rhs: tensor<2x5x7xf32>) {
  // expected-error @+1 {{found mismatching batch dimensions for lhs shape 'tensor<1x3x5xf32>' and rhs shape 'tensor<2x5x7xf32>'}}
  %0 = "tf.BatchMatMul"(%lhs, %rhs) : (tensor<1x3x5xf32>, tensor<2x5x7xf32>) -> tensor<2x3x7xf32>
}

// -----

  %1 = "tf.TensorListGetItem"(%0, %arg2, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32>
  %2 = "tf.TensorListGetItem"(%0, %arg3, %arg1) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<!tf_type.variant<tensor<1x32xf32>>>, tensor<i32>, tensor<2xi32>) -> tensor<1x32xf32>

// test Pad with invalid paddings rank
func.func @testPadWithInvalidPaddingsRank(tensor<2x1x3xf32>, tensor<1x3x2xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<1x3x2xi32>):
  // expected-error @+1 {{'tfl.pad' op failed to verify that operand 1 is 2-D}}
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<1x3x2xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>
}

// -----

    return success();
  }
};

// Fuses Unpack with proceeding Concatenation to Reshape if output type has
// static shape and activation function is none. For example:
//
//   // %input: tensor<1x3x2xf32>
//   %unpack:3 = "tfl.unpack"(%input) {axis = 1 : i32, num = 3 : i32}
//   %res = "tfl.concatenation"(%unpack#0, %unpack#1, %unpack#2)
//        {axis = -1 : i32, fused_activation_function = "NONE"}
//