// CHECK: %[[CALL:.+]] = "tf.XlaCallModule"(%[[ARG_0]], %[[DQ]])
// CHECK-SAME: _entry_function = @composite_conv_fn, _original_entry_function = "composite_conv_fn", _quantization_method = "weight_only_ptq { }"
// CHECK-SAME: (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x2x2x2xf32>
// CHECK: return %[[CALL]] : tensor<1x2x2x2xf32>

// -----

   - Variant runtime2 has attributes {custom=c2, org.gradle.status=${MultipleVariantSelectionIntegrationTest.defaultStatus()}}""")
    }

    void "cannot select distinct variants of the same component by using different attributes if they have the same capabilities"() {
        given:
        repository {
            'org:test:1.0' {
                variant('api') {
                    attribute('custom', 'c1')
                }

  // CHECK: "tf.IfRegion"
  // CHECK-SAME: is_stateless = false

  // CHECK: "tf.CalibrationStatisticsSaver"
  // CHECK-SAME: output_file_path = "serving_default_0.pb"

  // CHECK: "tf.CalibrationStatisticsSaver"
  // CHECK-SAME: output_file_path = "serving_default_1.pb"

  // CHECK: "tf.CalibrationStatisticsSaver"
  // CHECK-SAME: output_file_path = "serving_default_2.pb"

// CHECK-SAME: tf.entry_function = {inputs = "add_x:0,add_y:0", outputs = "add:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["main"]

// Check that the function call to @add exists and not to @NoOp.
// CHECK: %[[CALL0:.*]] = "tf.PartitionedCall"(%[[ARG0]], %[[ARG1]]) <{
// CHECK-NOT: f = @NoOp
// CHECK-SAME: f = @add
// CHECK-SAME: }>
// CHECK-SAME: : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

		numaInfo    *NUMAInfo
	}{
		{
			description: "current and candidate length is not the same, current narrower",
			current:     NewTestBitMask(0),
			candidate:   NewTestBitMask(0, 2),
			expected:    "current",
			numaInfo:    &NUMAInfo{},
		},
		{
			description: "current and candidate length is the same, distance is the same, current more lower bits set",
			current:     NewTestBitMask(0, 1),

// CHECK: "tf_saved_model.session_initializer"()
// CHECK-SAME: initializers = [@init_func_restore_op]

// CHECK: func.func @init_func_restore_op()
// CHECK-SAME: tf_saved_model.exported_names = ["tf_saved_model.session_initializer_restore_op"]
// CHECK-SAME: tf_saved_model.initializer_type = "restore_op"

			return u.nify(x.elem, y.elem, emode, p)
		}

	case *Struct:
		// Two struct types unify if they have the same sequence of fields,
		// and if corresponding fields have the same names, their (field) types unify,
		// and they have identical tags. Two embedded fields are considered to have the same
		// name. Lower-case field names from different packages are always different.
		if y, ok := y.(*Struct); ok {

			return u.nify(x.elem, y.elem, emode, p)
		}

	case *Struct:
		// Two struct types unify if they have the same sequence of fields,
		// and if corresponding fields have the same names, their (field) types unify,
		// and they have identical tags. Two embedded fields are considered to have the same
		// name. Lower-case field names from different packages are always different.
		if y, ok := y.(*Struct); ok {

  // CHECK: "tf.TPUReplicateMetadata"()
  // CHECK: [[RI_0:%.*]] = "tf.TPUReplicatedInput"([[ARG0]], [[ARG1]])
  // CHECK-SAME: is_packed = false
  // CHECK: [[RI_1:%.*]] = "tf.TPUReplicatedInput"([[ARG0]], [[ARG1]])
  // CHECK-SAME: is_packed = false
  // CHECK: [[PI:%.*]] = "tf.TPUPartitionedInputV2"([[RI_0]], [[RI_1]])
  // CHECK-SAME: is_packed = false
  "tf.TPUReplicateMetadata"() {num_cores_per_replica = 2 : i64, num_replicas = 2 : i64} : () -> ()

// Test that the locs are properly set to their shared_names.
// CHECK-LOC: "tf.VarHandleOp"() <{{{.*shared_name = "var_0".*}}}>
// CHECK-LOC-SAME: loc("var_0")
// CHECK-LOC: "tf.VarHandleOp"() <{{{.*shared_name = "var_1".*}}}>
// CHECK-LOC-SAME: loc("var_1")
}

// -----

// RestoreV2 op not created for `AssignVariableOp(VarHandleOp, Const)` patterns
// in the initializer function of "init_op" type.