def METHOD_NAME(self):
    """ Test ActivationSampler for a fully connected module """
    AimetLogger.set_level_for_all_areas(logging.DEBUG)
    model = TinyModel().eval()
    sim = QuantizationSimModel(model, dummy_input=torch.randn(1, 3, 32, 32), quant_scheme='tf_enhanced',
                               default_param_bw=4)
    for module in sim.model.modules():
        if isinstance(module, QcQuantizeWrapper):
            for quantizer in module.input_quantizers + module.output_quantizers:
                quantizer.enabled = False
                quantizer.enabled = False
    for quantizer in sim.model.conv1.input_quantizers + sim.model.conv1.output_quantizers:
        self.assertFalse(quantizer.encoding)
    self.assertTrue(sim.model.fc.param_quantizers['weight'])
    dataset_size = 100
    batch_size = 10
    image_size = (3, 32, 32)
    possible_batches = dataset_size // batch_size
    data_loader = create_fake_data_loader(dataset_size, batch_size, image_size)
    def forward_fn(model, inputs):
        inputs, _ = inputs
        model(inputs)
    act_sampler = ActivationSampler(model.fc, sim.model.fc, model, sim.model, forward_fn)
    quant_inp, orig_out = act_sampler.sample_and_place_all_acts_on_cpu(data_loader)
    self.assertEqual(list(quant_inp.shape), [batch_size * possible_batches, 36])
    self.assertEqual(list(orig_out.shape), [batch_size * possible_batches, 12])