simple(title, targets, desc=''):
      self.base(title, targets, desc)
      + options.legend.withCalcs([])
      + options.legend.withDisplayMode('list'),

    short(title, targets, desc=''):
      self.base(title, targets, desc)
      + timeSeries.standardOptions.withUnit('short')
      + timeSeries.standardOptions.withDecimals(0),

    seconds(title, targets, desc=''):
      self.base(title, targets, desc)

    """
    for sample_1, sample_2 in zip(repr_ds_1, repr_ds_2):
      self.assertCountEqual(sample_1.keys(), sample_2.keys())

      for input_key in sample_1:
        self._assert_tensorlike_all_close(
            sess, sample_1[input_key], sample_2[input_key]
        )

  def test_not_implemented_saver(self):
    with self.assertRaisesRegex(
        NotImplementedError, '"save" is not implemented.'
    ):

    ) {
        self.intercepted = "setTestString(String)";
        self.setTestString(newValue);
    }

    @InterceptGroovyCalls
    @CallableKind.GroovyPropertyGetter
    public static boolean intercept_testFlag(
        @ParameterKind.Receiver InterceptorTestReceiver self,
        @ParameterKind.CallerClassName String consumer
    ) {
        self.intercepted = "isTestFlag()";
        return self.isTestFlag();
    }

    }

    @InterceptGroovyCalls
    @InstanceMethod
    @WithExtensionReferences(toClass = ResourceGroovyMethods.class)
    public static void intercept_eachByte(
        @Receiver File self,
        int bufferLen,
        Closure<?> closure,
        @CallerClassName String consumer
    ) throws IOException {
        Instrumented.fileOpened(self, consumer);
        ResourceGroovyMethods.eachByte(self, bufferLen, closure);

      }

  def test_static_range_quantization_by_default(self):
    model = self.SimpleModel()

    saved_model_save.save(model, self._input_saved_model_path)

    # Use default QuantizationOptions.
    converted_model = quantize_model.quantize(
        self._input_saved_model_path,
        representative_dataset=self._simple_model_data_gen(),
    )

    self.assertIsNotNone(converted_model)
    self.assertCountEqual(

      tensor_spec.TensorSpec((), dtypes.float32)
  ])
  def compute(self, a, b):
    return (a + self.x) * (b + self.y) / (self.child.z) + self.child.c


class ReferencesParent(module.Module):

  def __init__(self, parent):
    super(ReferencesParent, self).__init__()
    self.parent = parent
    self.my_variable = variables.Variable(3., name="MyVariable")


# Creates a cyclic object graph.

    self.assertAllEqual(sq3.numpy().reshape(-1), [3, 6, 9, 12])

  def testBiasedDense(self):
    biased_dense = def_function.function(gen_composite_ops.my_biased_dense)
    t1 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    t2 = constant_op.constant([[1.0, 2.0], [3.0, 4.0]])
    t3 = constant_op.constant([[-10.0, -10.0], [-10.0, -10.0]])
    sq = biased_dense(t1, t2, t3)

go mod edit -require example.com/retract/self/prev@v1.1.0
go get example.com/retract/self/prev
go list -m example.com/retract/self/prev
stdout '^example.com/retract/self/prev v1.1.0$'

# 'go get pkg' should not downgrade from a retracted version when no higher
# version is available.
cp go.mod.orig go.mod
go mod edit -require example.com/retract/self/prev@v1.9.0
go get example.com/retract/self/prev

	def __init__(self, val):
		self.val = val

	def display_hint(self):
		return 'map'

	def to_string(self):
		return str(self.val.type)

	def children(self):
		MapBucketCount = 8 # see internal/abi.go:MapBucketCount
		B = self.val['B']
		buckets = self.val['buckets']
		oldbuckets = self.val['oldbuckets']
		flags = self.val['flags']
		inttype = self.val['hash0'].type

    # So hist_mids[i] = (lower_bound + bin_width / 2) + bin_width * i
    first_mid = self._lower_bound + self._bin_width / 2
    last_mid = first_mid + (self._num_bins - 1) * self._bin_width
    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)

  def _get_dequantized_hist_mids_after_quantize(
      self, quant_min: float, quant_max: float
  ) -> np.ndarray: