%0 = "tf_device.cluster_func"(%arg0, %arg1) {func = @_func, use_spmd_for_xla_partitioning = true, use_tpu = true, num_cores_per_replica = 2 : i64} : (tensor<2x4xf32>, tensor<4x2xf32>) -> tensor<2x2xf32>
  %1:2 = "tf.TPUPartitionedOutputV2"(%0) {device = "", partition_dims = [2, 1]} : (tensor<2x2xf32>) -> (tensor<1x2xf32>, tensor<1x2xf32>)

  // CHECK:  %5 = "tfl.arg_max"(%arg0, %cst) : (tensor<4x32x256xf32>, tensor<1xi32>) -> tensor<4x32xi32>
  // CHECK:  return %4, %5 : tensor<4x32xf32>, tensor<4x32xi32>
}

// CHECK-LABEL: func @convert_argmax_constant
func.func @convert_argmax_constant(%arg0: tensor<2x2x4xf32>) -> (tensor<2x2xf32>, tensor<2x2xi32>) {
  %0 = mhlo.constant dense<0xFF800000> : tensor<f32>

"/job:tpu_host_worker/replica:0/task:3/device:CPU:0"}, %arg15: tensor<*x!tf_type.resource<tensor<6x32xf32>>> {tf._user_specified_name = "927", tf.device = "/job:tpu_host_worker/replica:0/task:0/device:CPU:0"}, %arg16: tensor<*x!tf_type.resource<tensor<6x32xf32>>> {tf._user_specified_name = "929", tf.device = "/job:tpu_host_worker/replica:0/task:1/device:CPU:0"}, %arg17: tensor<*x!tf_type.resource<tensor<6x32xf32>>> {tf._user_specified_name = "931", tf.device = "/job:tpu_host_worker/replica:0/task:2/device:CPU:0"},...

// CHECK-LABEL: func @testWhileCasts(%arg0: tensor<!tf_type.variant<tensor<1x3xf32>>>) -> tensor<!tf_type.variant<tensor<*xf32>>>
func.func @testWhileCasts(%arg0: tensor<!tf_type.variant<tensor<1x3xf32>>>) -> (tensor<!tf_type.variant<tensor<*xf32>>>) {
  %0 = "tf.While"(%arg0) {
    cond = @testWhileCond, body = @testWhileBody, is_stateless = false
  } : (tensor<!tf_type.variant<tensor<1x3xf32>>>) -> (tensor<!tf_type.variant<tensor<*xf32>>>)

        return %2 : tensor<?x2xf32>
      }
      func.func private @composite_fn_1(%arg0: tensor<?x2xf32>, %arg1: tensor<2x2xf32>, %arg2: tensor<2xf32>) -> tensor<?x2xf32> attributes {_from_xla_call_module, tf_quant.composite_function} {
        return %arg0 : tensor<?x2xf32>
      }
    }
  )mlir";

// -----

// CHECK-LABEL: float_matmul
func.func @float_matmul(
  %arg0: tensor<1x10xf32>, %arg1: tensor<10x10xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
  %0 = "tf.MatMul"(%arg0, %arg1) {
    transpose_a = false, transpose_b = false
  } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

  %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]])

    "vhlo.return_v1" (%421) : (tensor<1xf32>) -> ()
   }) : (tensor<1x16x16x320xf32>, tensor<f32>) -> tensor<1x320xf32>
  return %0 : tensor<1x320xf32>
}

//CHECK:func.func private @reduce(%arg0: tensor<1x16x16x320xf32>, %arg1: tensor<f32>) -> tensor<1x320xf32> {
//CHECK-NEXT:  %0 = "vhlo.reduce_v1"(%arg0, %arg1) <{dimensions = #vhlo.tensor_v1<dense<[1, 2]> : tensor<2xi64>>}> ({ 

// RUN: tf-quant-opt %s -split-input-file -quant-merge-duplicate-resource-ops | FileCheck %s

func.func @merge_duplicate_variable(%arg0: tensor<1x20xf32>, %arg1: tensor<!tf_type.string>) -> (tensor<20x4096xf32>) {
  %0 = tf_executor.graph {
    %outputs_5, %control_6 = tf_executor.island wraps "tf.Const"() {value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>

// Tests for ops with canonicalization patterns.

// CHECK-LABEL: get_real_shape
func.func @get_real_shape(%arg0: tensor<1x2xf32>) -> tensor<2xindex> {
  %0 = "tfr.cast"(%arg0) : (tensor<1x2xf32>) -> !tfr.tensor
  %1 = tfr.get_shape %0 -> !shape.shape
  %2 = shape.to_extent_tensor %1 : !shape.shape -> tensor<2xindex>
  func.return %2 : tensor<2xindex>