import f2 "fmt"

// reflect.flag must not be visible in this package
type flag int
type _ reflect.flag /* ERROR "name flag not exported by package reflect" */

// imported package name may conflict with local objects
type reflect /* ERROR "reflect already declared" */ int

// dot-imported exported objects may conflict with local objects
type Value /* ERROR "Value already declared through dot-import of package reflect" */ struct{}

    --input-path=/path/to/model \
    [--exported-model-signatures=signature1,signature2] \
    [--tag-names=tag1,tag2] \
    [--input-arg-shapes-str=arg-name:shape,...] \
    [--e] \
    [--output-filename=/path/to/output.mlir]
```

* `--input-path`: The path to the input TensorFlow SavedModel or MLIR module
  with .mlir extension.
* `--exported-model-signatures`: Comma-separated list of exported model

						err = errors.New(string(ee.Stderr))
					}
					return
				}

				exports := strings.Split(string(bytes.TrimSpace(output)), "\n")
				if len(exports) != 1 {
					err = fmt.Errorf("go list reported %d exports; expected 1", len(exports))
					return
				}

				exportPath = exports[0]
			})

			return exportPath, err
		})
	}

	return f.(func() (string, error))()
}

        EclipseClasspathContainer container = project.classpathContainers.find { it.path == 'whenMergedContainerPath' }

        then:
        container != null
        container.exported == false
    }

    def "Classpath container can be configured"() {
        buildFile <<
        """apply plugin: 'java'
           apply plugin: 'eclipse'
           eclipse {
               classpath {

}

// All published variables.
var vars Map

// Publish declares a named exported variable. This should be called from a
// package's init function when it creates its Vars. If the name is already
// registered then this will log.Panic.
func Publish(name string, v Var) {
	if _, dup := vars.m.LoadOrStore(name, v); dup {
		log.Panicln("Reuse of exported var name:", name)
	}
	vars.keysMu.Lock()
	defer vars.keysMu.Unlock()

    if (exported_names.empty())
      return session_initializer.emitOpError()
             << "the initializer function should be exported";

    if (exported_names.size() != 1)
      return session_initializer.emitOpError()
             << "the initializer function should have only one exported names";
  }

  return success();
}

}  // namespace tf_saved_model
}  // namespace mlir

                <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.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>

    ],
)

# OSS: This is a header-only target. The implementation target `quantize_model_cc_impl` is
# directly linked to `lib_pywrap_tensorflow_internal.so`, so in most use cases of python-
# exported symbols depending directly on `quantize_model_cc_impl` should be unnecessary.
# Using the header-only target will help avoid the ODR violation.
cc_library(
    name = "quantize_model_cc",
    hdrs = ["quantize_model.h"],

      },
      R"pbdoc(
        Converts a TensorFlow SavedModel into StableHLO bytecode.

        * input-path: The path to the input TensorFlow SavedModel.
        * exported-model-signatures: Comma-separated list of exported model
          signatures to convert.
        * tag_names: Comma-separated list of tags for loading SavedModel.
        * input-arg-shapes: A string representation of input argument shapes for