}

// -----

// CHECK-LABEL: func @convolution_add_add_i8
func.func @convolution_add_add_i8(
    %lhs: tensor<?x3x2x1xi8>, %rhs: tensor<2x1x1x1xi8>,
    %zp_offset: tensor<?x2x2x1xi8>, %bias: tensor<1xi8>
  ) -> tensor<?x2x2x1xi8> {
  // CHECK-DAG: %[[conv:.*]] = mhlo.convolution
  // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]]

      %outputs_81, %control_82 = tf_executor.island wraps "tf.StridedSlice"(%outputs_79, %outputs_43, %outputs_45, %outputs_47) {begin_mask = 1 : i64, device = "/device:CPU:0", ellipsis_mask = 0 : i64, end_mask = 0 : i64, new_axis_mask = 0 : i64, shrink_axis_mask = 0 : i64} : (tensor<*xui8>, tensor<1xi32>, tensor<1xi32>, tensor<1xi32>) -> tensor<*xui8>

    %11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0] : (tensor<1xi8>) -> tensor<1x3x3x4xi8>
    %13 = stablehlo.convert %12 : (tensor<1x3x3x4xi8>) -> tensor<1x3x3x4xf32>
    %14 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>

    func.return %cst : tensor<1xi8>
  }

  // CHECK-LABEL: infer_var_handle_op_from_read
  func.func @infer_var_handle_op_from_read() -> tensor<1xi8> {
    %cst = arith.constant dense<1> : tensor<1xi8>
    %0 = "tf.VarHandleOp"() {container = "", shared_name = "bar"} : () -> tensor<!tf_type.resource<tensor<*xi8>>>
    // CHECK: "tf.VarHandleOp"() <{container = "", shared_name = "bar"}> : () -> tensor<!tf_type.resource<tensor<1xi8>>>

  %0 = "tfl.pseudo_qconst"() {qtype = tensor<1x!quant.uniform<i8:f32, 0.003:-128>>, value = dense<127> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 0.003:-128>>
  %1 = "tfl.pseudo_qconst"() {qtype = tensor<1x!quant.uniform<i8:f32, 0.003:-128>>, value = dense<127> : tensor<1xi8>} : () -> tensor<1x!quant.uniform<i8:f32, 0.003:-128>>