// CHECK-NEXT:   %[[RESULT_0:.*]] = "tf.While"(%[[ARG_0]])
// CHECK-SAME:   body = @while_body
// CHECK-SAME:   cond = @while_cond
// CHECK-SAME:   device = "/job:localhost/replica:0/task:0/device:CPU:0"
// CHECK-NEXT:   %[[RESULT_1:.*]] = tf_device.remote_run "/job:worker/replica:0/task:1" @[[MAIN_PARTITION_0:.*]](%[[ARG_1]])

			emon, epool, eimage := existingVolume.RBD.CephMonitors, existingVolume.RBD.RBDPool, existingVolume.RBD.RBDImage
			// two RBDs images are the same if they share the same Ceph monitor, are in the same RADOS Pool, and have the same image name
			// only one read-write mount is permitted for the same RBD image.
			// same RBD image mounted by multiple Pods conflicts unless all Pods mount the image read-only

	if cpid2 == ppid {
		t.Fatalf("Parent and child 2 have the same process ID")
	}

	if cpgrp2 == ppgrp {
		t.Fatalf("Parent and child 2 are in the same process group")
	}

	if cpid2 == cpgrp2 {
		t.Fatalf("Child 2's process group is its process ID")
	}

	if cpid1 == cpid2 {
		t.Fatalf("Child 1 and 2 have the same process ID")
	}

	if cpgrp1 != cpgrp2 {

// CKECK-SAME: <{calibration_method = 5 : i32, id = "skipping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

  // CHECK: tfrt.while [[pred_0]]#1 @"while_body_add2/tfrt_body_10"
  // CHECK-SAME: parallel_iterations(10)
  // CHECK: [[pred_1:%.*]]:2 = tfrt.call @"while_cond_lt9/tfrt_predicate"
  // CHECK: tfrt.while [[pred_1]]#1 @"while_body_add2/tfrt_body_1"
  // CHECK-SAME: parallel_iterations(1)

class ChannelIdentifierTest extends Specification {
    def "equals and hash code"() {
        def id = new ChannelIdentifier("channel")
        def same = new ChannelIdentifier("channel")
        def different = new ChannelIdentifier("other")

        expect:
        id == same
        id.hashCode() == same.hashCode()
        id != different
        id != null
        id != "channel"
    }

   (IsConstTensor $input_zp),
   (IsConstTensor $filter),
   (IsInt32ElementType $conv),
   (HasStaticShapeConstraint $filter),
   (HasStaticShapeAtDimsConstraint<"3"> $input)],
  [], (addBenefit 10)>;

// Same as ConvertTFConv2DToXLAConvOp but handles the case where input zero
// point is dynaically calculated so not a constant.
def ConvertTFConv2DToXLAConvOpDynamicRange : Pat<
  (TF_Conv2DOp:$conv

# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   [[ARG0:%.*]]: tensor<!tf_type.resource<tensor<5x3xf32>>> {tf_saved_model.bound_input = @[[VAR0]]},
# CHECK-SAME:   [[ARG1:%.*]]: tensor<!tf_type.resource<tensor<3x5xf32>>> {tf_saved_model.bound_input = @[[VAR1]]})
# CHECK-SAME:             -> (tensor<5x5xf32> {{{.*}}})
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"]

  func.return
}

// -----

// Tests last resource assign is returned even when the original function
// returns the same value prior.

// CHECK-LABEL: func @main
// CHECK-SAME: %{{[a-z0-9]+}}: tensor<f32> {tf.aliasing_output = 1 : i64}
// CHECK-SAME: %arg1: tensor<i1>
// CHECK-SAME: -> (tensor<f32>, tensor<f32>)
func.func @main(%arg0: tensor<!tf_type.resource<tensor<f32>>>, %arg1: tensor<i1>) -> tensor<f32> {

  // CHECK: %[[VAL_1:.*]] = "tf.Identity"(%[[VAL_0]]) : (tensor<*xf32>) -> tensor<*xf32>