* is implemented
     * */
    @RequiresInstalledToolChain(SUPPORTS_32)
    @ToBeFixedForConfigurationCache
    def "can have sourcefiles with same base name in same directory"() {
        setup:
        def testApp = new DuplicateMixedSameBaseNamesTestApp(AbstractInstalledToolChainIntegrationSpec.toolChain)


        testApp.getSourceFiles().each { SourceFile sourceFile ->

  }

  @Test
  fun testNoUncompress() {
    val response = response("https://httpbin.org/brotli", "XXXX".encodeUtf8())

    val same = uncompress(response)

    val responseString = same.body.string()
    assertThat(responseString).isEqualTo("XXXX")
  }

  @Test
  fun testFailsUncompress() {
    val response =

//
// Slice values are deeply equal when all of the following are true:
// they are both nil or both non-nil, they have the same length,
// and either they point to the same initial entry of the same underlying array
// (that is, &x[0] == &y[0]) or their corresponding elements (up to length) are deeply equal.
// Note that a non-nil empty slice and a nil slice (for example, []byte{} and []byte(nil))
// are not deeply equal.
//

  // CHECK-SAME: [7.000000e+00, 1.600000e+01], [9.000000e+00, 2.000000e+01], [1.100000e+01, 2.400000e+01]
  // CHECK-SAME: [1.300000e+01, 2.800000e+01], [1.500000e+01, 3.200000e+01], [1.700000e+01, 3.600000e+01]
  // CHECK: %[[CONV:.*]] = "tf.Conv2D"(%arg0, %[[CST]]) <{data_format = "NHWC", dilations = [1, 2, 3, 1], explicit_paddings = [], padding = "SAME", strides = [1, 4, 5, 1], use_cudnn_on_gpu = true}>
  // CHECK: return %[[CONV]] : tensor<1x28x23x2xf32>
}

It's illegal to find two components providing the same _capability_ in a single dependency graph.
Intuitively, it means that if Gradle finds two components that provide the same thing on classpath, it's going to fail with an error indicating what modules are in conflict.
In our example, it means that different bindings of a logging framework provide the same capability.

== Capability coordinates

// CHECK-SAME: effective_hidden_scale_intermediate = tensor<*x!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>
// CHECK-SAME: input_to_cell_intermediate = tensor<*x!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>
// CHECK-SAME: input_to_forget_intermediate = tensor<*x!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>

# CHECK:    tf_executor.graph
# CHECK:      "tf.VarHandleOp"()
# CHECK:      "tf.LegacyCall"
# CHECK-SAME:   <{_disable_call_shape_inference = true, f = @test_func_name0}> {device = ""}
# CHECK:      tf_executor.fetch
# CHECK:    return
# CHECK:  func private @test_func_name0
# CHECK-SAME:   tf._resource_arg_unique_id = 0
# CHECK-SAME:   tf._resource_arg_unique_id = 0
# CHECK:    tf_executor.graph
# CHECK:      tf_executor.fetch

  %3 = "tf.AddV2"(%2, %1): (tensor<15x28x28x1xf32>, tensor<1xf32>) -> tensor<15x28x28x1xf32>
  func.return %2 : tensor<15x28x28x1xf32>

// RUN: tf-opt -tf-tpu-resource-read-for-write %s | FileCheck %s --dump-input=always

// CHECK-LABEL: func @write_only_resource
// CHECK-SAME: ([[ARG0:%.*]]: tensor<i32>, [[ARG1:%.*]]: tensor<f32>, [[ARG2:%.*]]: tensor<*x!tf_type.resource<tensor<i32>>>)
func.func @write_only_resource(%arg0: tensor<i32>, %arg1: tensor<f32>, %arg2: tensor<*x!tf_type.resource<tensor<i32>>>) {
  // CHECK-NEXT: [[READ:%.*]] = "tf.ReadVariableOp"([[ARG2]])

apiVersion: gateway.networking.k8s.io/v1beta1
kind: Gateway
metadata:
  name: duplicate-listeners
spec:
  gatewayClassName: acme-lb
  listeners:
  - name: same
    protocol: HTTP
    port: 80
  - name: same
    protocol: HTTP