// CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[maxpool]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[reshape:.*]] = "tf.Reshape"(%[[dq1]]
// CHECK: %[[q2:.*]] = "quantfork.qcast"(%[[reshape]])
// CHECK-SAME: quant.uniform<i8:f32, 5.000000e-02:-10>
// CHECK: %[[dq2:.*]] = "quantfork.dcast"(%[[q2]])

  // CHECK-SAME: %arg21: tensor<!tf_type.resource<tensor<640xf32>>>
  // CHECK-SAME: %arg22: tensor<!tf_type.resource<tensor<640xf32>>>
  // CHECK-SAME: %arg23: tensor<!tf_type.resource<tensor<512x640xf32>>>
  // CHECK-SAME: %arg24: tensor<!tf_type.resource<tensor<640x640xf32>>>
  // CHECK-SAME: %arg25: tensor<!tf_type.resource<tensor<4096xf32>>>
  // CHECK-SAME: %arg26: tensor<!tf_type.resource<tensor<640x4096xf32>>>

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-SAME: is_mutable
  // CHECK-SAME: }> : () -> ()
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.> : tensor<f32> } : () -> ()

  // CHECK: "tf_saved_model.global_tensor"() <{
  // CHECK-SAME: is_mutable
  // CHECK-SAME: }> : () -> ()

  // CHECK-SAME: _XlaSharding = ""
  // CHECK-SAME: partition_dims = []
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG0]], [[OUTPUT]]#0)
  // CHECK-DAG:  "tf.AssignVariableOp"([[ARG1]], [[OUTPUT]]#1)
  "tf.AssignVariableOp"(%0, %2) : (tensor<!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @read_write_packed_resource

// RUN: tf-opt -split-input-file -verify-diagnostics -xla-legalize-tf-collective -xla-legalize-tf %s | FileCheck %s


// CHECK: module attributes
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_key = 0
// CHECK-SAME{LITERAL}: tf2xla.collective_info.group_size = 2
// CHECK-LABEL: func @all_reduce_cross_replica
func.func @all_reduce_cross_replica(%input: tensor<f32>) -> tensor<f32> {

  }

// CHECK-LABEL: func @conv_with_single_layer

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]]
// CHECK-SAME: f = @quantized_conv2d_with_bias_and_relu6_float_output_fn_0

    return %arg2 : tensor<?xi32>
  }

  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-SAME:   dim_args_spec = []
    // CHECK-NOT:    _entry_function

// tf_device.Cluster.

// CHECK-LABEL: func @testReadVariableOpColocated
// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate
  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)

  func.return
}

// CHECK:      tf_device.replicate
// CHECK-SAME: [%[[ARG_0]], %[[ARG_0]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_2]], %[[ARG_2]]] as %{{[a-z0-9]*}}
// CHECK-SAME: [%[[ARG_4]], %[[ARG_4]]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_1]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_3]] as %{{[a-z0-9]*}}
// CHECK-SAME: %[[ARG_5]] as %{{[a-z0-9]*}}

	netns1 := p.UpsertPodCacheWithNetns(string(pod.UID), wl)
	if !reflect.DeepEqual(netns1, ns) {
		t.Fatalf("Expected the same Netns for the same uid, got %v and %v", netns1, ns)
	}

	ns2 := newFakeNsInode(inc(), 1)
	wl2 := WorkloadInfo{
		Workload: podToWorkload(pod),
		Netns:    ns2,
	}
	// when using same uid, the original netns should be returned
	netns2 := p.UpsertPodCacheWithNetns(string(pod.UID), wl2)
	if netns2 != ns {