// CHECK: tf.GeneratorDatasetRegion
  // CHECK: ^
  // CHECK-SAME: tensor<4xf32>
  // CHECK: func.call @init
  // CHECK: ^
  // CHECK-SAME: tensor<7xf32>
  // CHECK-SAME: tensor<3xf32>
  // CHECK-NOT: tf_type.resource
  // CHECK: func.call @next
  // CHECK: ^
  // CHECK-SAME: tensor<6xf32>
  // CHECK-SAME: tensor<2xf32>
  // CHECK: func.call @finalize
  // CHECK-NOT: tf.GeneratorDataset

}

// CHECK: %[[maxpool_i8:.*]] = "tf.MaxPool"
// CHECK-SAME: (tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[reshape_i8:.*]] = "tf.Reshape"(%[[maxpool_i8]]
// CHECK-SAME: (tensor<*xi8>, tensor<2xi32>) -> tensor<*xi8>
// CHECK: %[[scast:.*]] = "quantfork.scast"(%[[reshape_i8]]
// CHECK: %[[matmul:.*]] = "tf.PartitionedCall"(%[[scast]]
// CHECK-SAME: f = @composite_matmul_fn_1

// CHECK-LABEL: module attributes {tf_saved_model.semantics}
module attributes {tf_saved_model.semantics} {
  // 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> } : () -> ()

// MergeGateways combines multiple gateways targeting the same workload into a single logical Gateway.
// Note that today any Servers in the combined gateways listening on the same port must have the same protocol.
// If servers with different protocols attempt to listen on the same port, one of the protocols will be chosen at random.

// CHECK:             tfl.yield
// CHECK-SAME:  (tensor<i1>) -> ()
// CHECK:             [[VAL_30:%.*]]:7 =
// CHECK: call @tfl.while_body
// CHECK:             tfl.yield
// CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<*xf32>, tensor<4x2xf32>, tensor<4x2xf32>, tensor<*xf32>, tensor<4x4x3xf32>) -> ()

// CHECK-LABEL:   func private @tfl.while_cond(

module {
  // CHECK-LABEL: func @singlecluster
  // CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<?xi32>)
  func.func @singlecluster(%arg0: tensor<?xi32>) -> tensor<?xi32> {
    // CHECK: %[[A_OUTPUT:[0-9]*]] = "tf.A"(%[[ARG_0]])
    %2 = "tf.A"(%arg0) : (tensor<?xi32>) -> tensor<?xi32>

    // CHECK: %[[TPU0_OUTPUT:[0-9]*]] = "tf_device.launch"
    // CHECK-SAME: <{device = "tpu0"}>

This will be especially useful when you use it in a **large code base** where you use **the same dependencies** over and over again in **many *path operations***.

## To `async` or not to `async`

As dependencies will also be called by **FastAPI** (the same as your *path operation functions*), the same rules apply while defining your functions.

// 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"