TF_Status* s = TF_NewStatus();
  TF_OpKernelConstruction_GetAttrType(ctx, "foobar", &type, s);
  TF_DeleteStatus(s);
  return NULL;
}

// A compute function. This will never actually get called in this test, it's
// just nice to know that it compiles.
void compute(void* kernel, TF_OpKernelContext* ctx) {
  TF_Tensor* input;
  TF_Status* s = TF_NewStatus();
  TF_GetInput(ctx, 0, &input, s);
  TF_DeleteTensor(input);

                 TF_Message(first_bad_status.get()));
    return result;
  }
  // For each output of the original operation, pack the per-device
  // TensorHandles we've computed into a single parallel TensorHandle.
  std::vector<std::unique_ptr<ParallelTensor>> per_device_outputs;
  per_device_outputs.reserve(first_op_output_count);
  for (int i = 0; i < first_op_output_count; ++i) {

 * The flatten phase will keep only a single version per ({@code groupId}, {@code artifactId}) pair.
 * The resolution will actually download the dependencies (or artifacts) that have been computed.
 *
 * @since 4.0.0
 */
@Experimental
@Immutable
public interface Dependency extends Artifact {
    /**
     * {@return the type of the dependency}
     * A dependency can be a <abbr>JAR</abbr> file,

 
-     The choice of symbol is arbitrary. The static address we obtain
-     by constructing a non GOT reference to the symbol, the dynamic
-     address of the symbol we compute using adrp/add to compute the
-     symbol's address relative to the PC. */
-
-  ElfW(Addr) static_addr;
-  ElfW(Addr) dynamic_addr;
-
-  asm ("					\n\
-	adrp	%1, _dl_start;			\n\

        "//tensorflow/compiler/mlir/quantization/tensorflow:passes",
        "//tensorflow/compiler/mlir/tensorflow",
        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",
        "//tensorflow/compiler/mlir/tensorflow:error_util",
        "//tensorflow/compiler/mlir/tensorflow:mlir_import_options",

        "@local_xla//xla/tsl/c:tsl_status",
    ] + select({
        "//tensorflow:with_xla_support": [
            "//tensorflow/compiler/jit",
            "//tensorflow/compiler/tf2xla:xla_compiler",
        ],
        "//conditions:default": [],
    }) + if_tensorrt([
        "//tensorflow/compiler/tf2tensorrt:trt_convert_api",
    ]),
)

# Check that c_api_no_xla does not depend on xla.
check_deps(

              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.api=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.file=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.model=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.parser=ALL-UNNAMED</arg>

              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.api=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.file=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.main=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.model=ALL-UNNAMED</arg>
              <arg>-J--add-exports=jdk.compiler/com.sun.tools.javac.parser=ALL-UNNAMED</arg>

	}

	// Now, we read the payload and compute its SHA-256 hash.
	_, err = io.ReadFull(cr.reader, cr.buffer)
	if err == io.EOF && size != 0 {
		err = io.ErrUnexpectedEOF
	}
	if err != nil && err != io.EOF {
		cr.err = err
		return n, cr.err
	}

	// Once we have read the entire chunk successfully, we verify
	// that the received signature matches our computed signature.
	cr.chunkSHA256Writer.Write(cr.buffer)

    }

    def "matches kotlin compiler on linux"() {