* have to specify the context of a configuration statement multiple times. <p> Instead of:</p>
     * <pre>
     * MyType myType = new MyType()
     * myType.doThis()
     * myType.doThat()
     * </pre>
     * <p> you can do:
     * <pre>
     * MyType myType = configure(new MyType()) {
     *     doThis()
     *     doThat()
     * }
     * </pre>
     *

				toVisit = append(toVisit, testPath{path: visit.path + "[*]", value: item})
			} else if !isPrimitive(visit.value.Type().Elem().Kind()) {
				t.Logf("unhandled non-primitive map type %s: %s", visit.path, visit.value.Type().Elem())
			}

		case visit.value.Kind() == reflect.Pointer:
			if visit.value.IsNil() {
				if visit.value.Type().Elem().Kind() == reflect.Struct {

}

// Map contains Type fields specific to maps.
type Map struct {
	Key  *Type // Key type
	Elem *Type // Val (elem) type

	Bucket *Type // internal struct type representing a hash bucket
}

// MapType returns t's extra map-specific fields.
func (t *Type) MapType() *Map {
	t.wantEtype(TMAP)
	return t.extra.(*Map)
}

// Forward contains Type fields specific to forward types.
type Forward struct {

        rng.uniform(low=0.0, high=1.0, size=static_input_shape).astype(
            np.float32
        )
    )

    def data_gen() -> repr_dataset.RepresentativeDataset:
      for _ in range(100):
        yield {
            'input_tensor': rng.uniform(
                low=0.0, high=1.0, size=static_input_shape
            ).astype(np.float32)
        }

func typeOffFor(t *abi.Type, off aTypeOff) *abi.Type {
	return toRType(t).typeOff(off)
}

func (t *interfaceType) typeOff(off aTypeOff) *abi.Type {
	return toRType(&t.Type).typeOff(off)
}

func (t *interfaceType) common() *abi.Type {
	return &t.Type
}

func (t *interfaceType) uncommon() *abi.UncommonType {
	return t.Uncommon()
}

// mapType represents a map type.
type mapType struct {
	abi.MapType

            .configure(ModelActionRole.Initialize) { it.path("foo").type(Bean).action(action) }
            .configure(ModelActionRole.Mutate) { it.path("foo").type(Bean).action(action) }
            .configure(ModelActionRole.Finalize) { it.path("foo").type(Bean).action(action) }
            .configure(ModelActionRole.Validate) { it.path("foo").type(Bean).action(action) }

        when:

              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  name='x', shape=x_signature, dtype=dtypes.float32
              )
          ]

		// void
		{`package n0; func f() { f() }`, `f()`, `void`},

		// types
		{`package t0; type _ int`, `int`, `type`},
		{`package t1; type _ []int`, `[]int`, `type`},
		{`package t2; type _ func()`, `func()`, `type`},
		{`package t3; type _ func(int)`, `int`, `type`},
		{`package t3; type _ func(...int)`, `...int`, `type`},

		// built-ins
		{`package b0; var _ = len("")`, `len`, `builtin`},

		// void
		{`package n0; func f() { f() }`, `f()`, `void`},

		// types
		{`package t0; type _ int`, `int`, `type`},
		{`package t1; type _ []int`, `[]int`, `type`},
		{`package t2; type _ func()`, `func()`, `type`},
		{`package t3; type _ func(int)`, `int`, `type`},
		{`package t3; type _ func(...int)`, `...int`, `type`},

		// built-ins
		{`package b0; var _ = len("")`, `len`, `builtin`},

        }
    }

    /**
     * Maps [typeArguments] to the type parameters of [partiallyAppliedSymbol].
     *
     * If too many type arguments are provided, a mapping is still created. Extra type arguments are simply ignored. If this wasn't the
     * case, the resulting [KaCall] would contain no type arguments at all, which can cause problems later. If too few type arguments are