}

    def "artifacts with the same id but different content are transformed independently"() {
        def repo1 = new MavenFileRepository(testDirectory.file("repo1"))
        def repo2 = new MavenFileRepository(testDirectory.file("repo2"))
        def m1 = repo1.module("test", "test", "1.3").publish()
        m1.artifactFile.text = "1234"
        def m2 = repo2.module("test", "test", "1.3").publish()

        def outputDir1 = gradleUserHomeOutputDir("lib1.jar", "lib1.jar.txt")
        def outputDir2 = gradleUserHomeOutputDir("lib2.jar", "lib2.jar.txt")
        def outputDir3 = gradleUserHomeOutputDir("lib3.jar", "lib3.jar.txt")
        def outputDir4 = gradleUserHomeOutputDir("lib4-1.0.jar", "lib4-1.0.jar.txt")

        when:

def TFL_I4 : I<4>;
def TFL_Int32Or64 : SignlessIntOfWidths<[32, 64]>;

def TFL_BoolTensor : TFL_TensorOf<[I1]>;
def TFL_FpTensor : TFL_TensorOf<[F32]>;
def TFL_I32OrI64Tensor : TFL_TensorOf<[TFL_Int32Or64]>;
def TFL_I32Tensor : TFL_TensorOf<[I32]>;
def TFL_I64Tensor : TFL_TensorOf<[I64]>;
def TFL_Complex64Tensor : TFL_TensorOf<[Complex<F<32>>]>;
def TFL_ResourceTensor : TFL_TensorOf<[TF_Resource]>;

     \\--- conf
"""
    }

    def "can limit the report to one path to each dependency"() {
        given:
        mavenRepo.with {
            def leaf = module('org', 'leaf').publish()
            def a = module('org', 'a').dependsOn(leaf).publish()
            def b = module('org', 'b').dependsOn(a).publish()
            def c = module('org', 'c').dependsOn(a).publish()
            def d = module('org', 'd').dependsOn(leaf).publish()

)


def _is_variable(node_def: node_def_pb2.NodeDef) -> bool:
  """Determines whether `node_def` is a variable node.

  Args:
    node_def: `NodeDef` to test whether it is a variable or not.

  Returns:
    Returns True if it is a variable.
  """
  return node_def.op == 'VarHandleOp'


def _find_variables(
    graph_def: graph_pb2.GraphDef,

    MLIR pipeline in the future.
  }];
  let constructor = "TF::CreateGpuOpFusionPass()";
}

def BatchMatMulToEinsumPass : Pass<"tf-batch-matmul-to-tf-einsum", "mlir::func::FuncOp"> {
  let summary = "Replace TF BatchMatMul op by TF Einsum op.";
  let constructor = "TF::CreateBatchMatMulToEinsumPass()";
}

def CanonicalizeCompileAndReplicateAttributesPass : Pass<"tf-canonicalize-compile-and-replicate-attributes", "mlir::func::FuncOp"> {

    if (outside_compilation_node->IsIdentity()) {
      TF_RETURN_IF_ERROR(
          GetNodeAttr(outside_compilation_node->def(), "T", &data_type));
    } else {
      TF_RETURN_IF_ERROR(
          GetNodeAttr(outside_compilation_node->def(), "dtype", &data_type));
    }
    data_types.push_back(data_type);
  }
  n->ClearAttr(type_attr_name);
  n->AddAttr(type_attr_name, data_types);

      Request(
        url = server.url("/"),
        body = "def".toRequestBody("text/plain".toMediaType()),
      )
    executeSynchronously(request)
      .assertCode(200)
      .assertBody("abc")
    val recordedRequest = server.takeRequest()
    assertThat(recordedRequest.method).isEqualTo("POST")
    assertThat(recordedRequest.body.readUtf8()).isEqualTo("def")
    assertThat(recordedRequest.headers["Content-Length"]).isEqualTo("3")

        return decorator

    def websocket_route(
        self, path: str, name: Union[str, None] = None
    ) -> Callable[[DecoratedCallable], DecoratedCallable]:
        def decorator(func: DecoratedCallable) -> DecoratedCallable:
            self.add_websocket_route(path, func, name=name)
            return func

        return decorator

    def include_router(
        self,

            )
        return self.openapi_schema

    def setup(self) -> None:
        if self.openapi_url:
            urls = (server_data.get("url") for server_data in self.servers)
            server_urls = {url for url in urls if url}

            async def openapi(req: Request) -> JSONResponse:
                root_path = req.scope.get("root_path", "").rstrip("/")