%cst = "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %cst_0 = "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %0 = "tf.AddV2"(%cst, %cst_0) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %1 = "tf.Max"(%arg0, %0) {device = ""} : (tensor<1x2x3xf32>, tensor<i32>) -> tensor<1x3xf32>

  %3 = "tfl.dequantize"(%2) : (tensor<1x3x3x!quant.uniform<i8:f32, 0.004:-128>>) -> tensor<1x3x3xf32>
  %4 = "tfl.div"(%arg0, %3) {fused_activation_function = "NONE"} : (tensor<1x3x3xf32>, tensor<1x3x3xf32>) -> tensor<1x3x3xf32>
  func.return %4 : tensor<1x3x3xf32>
//  CHECK: %[[logistic:.*]] = "tfl.logistic"
//  CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[logistic]])

  // CHECK-LABEL: testDilatedConv1DExpandH
  // CHECK-SAME: ([[INPUT:%.*]]: tensor<1x128x3xf32>, [[FILTER:%.*]]: tensor<1x5x3x8xf32>)
  // CHECK-NEXT: [[AXIS:%.*]] = "tf.Const"() <{value = dense<-3> : tensor<i32>}> : () -> tensor<i32>
  // CHECK-NEXT: [[EXPAND:%.*]] = "tf.ExpandDims"([[INPUT]], [[AXIS]]) : (tensor<1x128x3xf32>, tensor<i32>) -> tensor<1x1x128x3xf32>

      %6 = "tf.Identity"(%5) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32>
      "tf.Yield"(%4, %6) {device = ""} : (tensor<i1>, tensor<1x3xf32>) -> ()
    }) {_lower_using_switch_merge = true, _read_only_resource_inputs = [], device = ""} : (tensor<i1>) -> (tensor<i1>, tensor<1x3xf32>)
    %3 = "tf.Identity"(%2#1) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32>

//CHECK:func.func private @slice(%arg0: tensor<160x20x1xf32>) -> tensor<1x1x1xf32> {
//CHECK-NEXT: %0 = "vhlo.slice_v1"(%arg0) <{limit_indices = #vhlo.tensor_v1<dense<0> : tensor<3xi64>>, start_indices = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>, strides = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>}> : (tensor<160x20x1xf32>) -> tensor<1x1x1xf32> 
//CHECK-NEXT: return %0 : tensor<1x1x1xf32>
//CHECK-NEXT:}

  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>
  %2 = "tfl.quantize"(%1) {qtype = tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>} : (tensor<1x2x3xf32>) -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>

  // CHECK: [[Result:%.*]] = "tf.Case"(%arg0, %arg1)
  // CHECK-SAME: branches = [@tf.CaseRegion_branch0{{.*}}, @tf.CaseRegion_branch1{{.*}}]
  // CHECK-SAME: is_stateless = false
  %0 = "tf.CaseRegion"(%arg0) ({
    %1 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<!tf_type.resource>

func.func @convert_dot_general_dynamic_batch_dim(%arg0: tensor<2x?x2x3xf32>, %arg1: tensor<2x?x4x3xf32>) -> tensor<2x?x2x4xf32> {
%0 = "mhlo.dot_general"(%arg0, %arg1) {
  dot_dimension_numbers = #mhlo.dot<
    lhs_batching_dimensions = [0, 1],
    rhs_batching_dimensions = [0, 1],
    lhs_contracting_dimensions = [3],
    rhs_contracting_dimensions = [3]
  >} : (tensor<2x?x2x3xf32>, tensor<2x?x4x3xf32>) -> tensor<2x?x2x4xf32>