%cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
  %1 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = true
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>
  %2 = "tf.Reshape"(%1, %cst_0) : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>

    %4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

// CHECK-SAME: (tensor<1x2x2x3xi8>, tensor<2x2x3x2xi8>, tensor<2xi32>, tensor<f32>, tensor<i32>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>, tensor<f32>, tensor<i32>) -> tensor<*xf32>
// CHECK: return %[[conv_quant]]

// CHECK-LABEL: func private @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

  constexpr char kMlirIllegalOpStr[] = R"(
  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.DoesntExist"() : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";
  CellReader<int64_t> legalize_failure_count(
      "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count");

  return %0 : tensor<*xi32>
}
func.func private @_batched(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  return %arg0 : tensor<1xi32>
}

}

// -----

module attributes {tf_saved_model.semantics} {

// Test hoisting write side-effect ops.

    %cst = "tf.Const"() <{value = dense<[1]> : tensor<1xi32>}> : () -> tensor<1xi32>
    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<2x3xbf16>>>
    %1 = "tf.ResourceGather"(%0, %cst) <{batch_dims = 0 : i64, validate_indices = true}> : (tensor<!tf_type.resource<tensor<2x3xbf16>>>, tensor<1xi32>) -> tensor<1x3xbf16>
    return %1: tensor<1x3xbf16>
  }

func.func @listPopBack(%arg0: tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<1xi32>) -> (tensor<!tf_type.variant<tensor<2xi32>>>, tensor<2xi32>)  {
  %0, %1 = "tf.TensorListPopBack"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<2xi32>>>, tensor<1xi32>) -> (tensor<!tf_type.variant<tensor<2xi32>>>, tensor<2xi32>)

  %dense_default_0 = "tf.Const"() {device = "/device:CPU:0", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %dense_default_1 = "tf.Const"() {device = "/device:CPU:0", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %dense_keys = "tf.Const"() {device = "/device:CPU:0", dtype = !tf_type.string, value = dense<""> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>

  %3:2 = "tf.TPUReplicatedOutput"(%2#0) : (tensor<?xi32>) -> (tensor<?xi32>, tensor<?xi32>)
  %5:2 = "tf.TPUPartitionedOutputV2"(%3#0) {_XlaSharding = "", partition_dims = []} : (tensor<?xi32>) -> (tensor<?xi32>, tensor<?xi32>)
  %7:2 = "tf.TPUPartitionedOutputV2"(%3#1) {_XlaSharding = "", partition_dims = []} : (tensor<?xi32>) -> (tensor<?xi32>, tensor<?xi32>)

    %cst = "tf.Const"() {value = dense<-1> : tensor<i64>} : () -> tensor<i64>
    %cst_0 = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
    %cst_1 = "tf.Const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
    %cst_2 = "tf.Const"() {value = dense<0.00235294132> : tensor<f32>} : () -> tensor<f32>