def METHOD_NAME(sim_model):
    """
    The User of the QuantizationSimModel API is expected to write this function based on their data set.
    This is not a working function and is provided only as a guideline.
    :param sim_model:
    :return:
    """
    # User action required
    # The following line is an example of how to use the ImageNet data's validation data loader.
    # Replace the following line with your own dataset's validation data loader.
    data_loader = ImageNetDataPipeline.get_val_dataloader()
    # User action required
    # For computing the activation encodings, around 1000 unlabelled data samples are required.
    # Edit the following 2 lines based on your batch size.
    # batch_size * max_batch_counter should be 1024
    batch_size = 64
    max_batch_counter = 16
    sim_model.eval()
    current_batch_counter = 0
    with torch.no_grad():
        for input_data, target_data in data_loader:
            inputs_batch = input_data  # labels are ignored
            sim_model(inputs_batch)
            current_batch_counter += 1
            if current_batch_counter == max_batch_counter:
                break