Project: s41467-024-51639-5 | Function: calculate_loss (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Implements the core incremental learning loss calculation that combines CTC loss and RBKD (Response-Based Knowledge Distillation) loss to enable nanopore basecaller adaptation for modification detection while preventing catastrophic forgetting

**KEY TERMS:** {
  "loss_ctc": "CTC loss L_CTC for sequence prediction",
  "loss_kd": "Knowledge distillation loss L_RBKD",
  "lambda_value": "hyperparameter λ for balancing losses",
  "outputs": "student model CTC matrix U",
  "teacher_outputs": "teacher model CTC matrix V",
  "ctc.crf_flipflop_loss": "CTC loss computation function",
  "kl_divergence": "KL divergence for knowledge distillation"
}

**CONTEXT CODE:**         #chunk_batch = [chunk for chunk in chunk_batch if chunk.start_sample > 5000]
        first_strand_index += sum(batch_rejections.values())
        if len(chunk_batch) < sub_batch_size:
            log.write(('* Warning: only {} chunks passed filters ' +
                       '(asked for {}).\n').format(
                           len(chunk_batch), sub_batch_size))

        if not all(chunk.seq_len > 0.0 for chunk in chunk_batch):
            raise Exception('Error: zero length sequence')

        # Shape of input tensor must be:
        #     (timesteps) x (batch size) x (input channels)
        # in this case:
        #     batch_chunk_len x sub_batch_size x 1
        stacked_current = np.vstack([
            revop(chunk.current) for chunk in chunk_batch]).T
        indata = torch.tensor(stacked_current, device='cpu',
                              dtype=torch.float32).unsqueeze(2)
        if pin and torch.cuda.is_available():
            indata = indata.pin_memory()

        # Prepare seqs, seqlens and (if necessary) mod_cats
        seqs, seqlens = [], []
        mod_cats = [] if net_info.metadata.is_cat_mod else None
        for chunk in chunk_batch:
            chunk_labels = revop(chunk.sequence)
            seqlens.append(len(chunk_labels))
            if net_info.metadata.is_cat_mod:
                chunk_mod_cats = np.ascontiguousarray(
                    net_info.metadata.mod_labels[chunk_labels])
                mod_cats.append(chunk_mod_cats)
                # convert chunk_labels to canonical base labels
                chunk_labels = np.ascontiguousarray(
                    net_info.metadata.can_labels[chunk_labels])
            chunk_seq = flipflopfings.flipflop_code(
                chunk_labels, alphabet_info.ncan_base)
            seqs.append(chunk_seq)

        seqs = torch.tensor(
            np.concatenate(seqs), dtype=torch.long, device='cpu')
        seqlens = torch.tensor(seqlens, dtype=torch.long, device='cpu')
        if net_info.metadata.is_cat_mod:
            mod_cats = torch.tensor(
                np.concatenate(mod_cats), dtype=torch.long, device='cpu')

        total_sub_batches += 1

        yield indata, seqs, seqlens, mod_cats, sub_batch_size, batch_rejections


def calculate_loss(
        net_info, batch_gen, sharpen, mod_cat_weights=None, mod_factor=None,
        calc_grads=False, lambda_value = 1.0):
    """Implements the core incremental learning loss calculation that combines CTC loss and RBKD (Response-Based Knowledge Distillation) loss to enable nanopore basecaller adaptation for modification detection while preventing catastrophic forgetting"""
    raise NotImplementedError("Function implementation has been masked")


def apply_clipping(net_info, grad_max_threshs):
    parameters = [p for p in net_info.net_mod.parameters() if p.requires_grad]
    grad_maxs = [
        float(torch.max(torch.abs(param_group.grad.detach())))
        for param_group in parameters]
    if grad_max_threshs is not None:
        for grp_gm, grp_gmt, grp_params in zip(
                grad_maxs, grad_max_threshs, parameters):
            if grp_gm > grp_gmt:
                # clip norm by value
                grp_params.grad.data.clamp_(min=-grp_gmt, max=grp_gmt)
    return grad_maxs


def parse_init_args(args):
    is_multi_gpu = (args.local_rank is not None)
    is_lead_process = (not is_multi_gpu) or args.local_rank == 0

    # if seed is provided use this else generate random seed value
    seed = (
        np.random.randint(0, np.iinfo(np.uint32).max, dtype=np.uint32)
        if args.seed is None else args.seed)

    main_log_fn = os.path.join(args.outdir, MODEL_LOG_FILENAME)
    if is_lead_process:
        helpers.prepare_outdir(args.outdir, args.overwrite)
        if args.model.endswith('.py'):
            copyfile(args.model, os.path.join(args.outdir, 'model.py'))
        # note buffering=1 to enforce line buffering and enable
        # inspection/plotting during a run
        logs = LOGS(
            main=helpers.Logger(main_log_fn, args.quiet),
            batch=open(os.path.join(args.outdir, BATCH_LOG_FILENAME),
                       'w', buffering=1),
            validation=open(os.path.join(args.outdir, VAL_LOG_FILENAME),
                            'w', buffering=1))
        logs.batch.write(BATCH_HEADER)
        logs.validation.write(VAL_HEADER)

        if args.save_every % DOTROWLENGTH != 0:
            # Illegal save_every, change
            se2 = int(math.ceil(args.save_every / DOTROWLENGTH)) * DOTROWLENGTH
            logs.main.write('* --save_every {} not a multiple of {}, rounding '
                            'to {}'.format(args.save_every, DOTROWLENGTH, se2))
            # args.save_every = se2

        if args.chunk_len_min > args.chunk_len_max:
            # Illegal chunk length parameters
            raise ValueError('--chunk_len_min greater than --chunk_len_max')

        logs.main.write('* Using random seed: {}\n'.format(seed))
    else:
        logs = LOGS(main=helpers.Logger(main_log_fn, args.quiet))

    if is_multi_gpu:
        # Use distributed parallel processing to run one process per GPU
        try:
            torch.distributed.init_process_group(backend='nccl')
        except Exception:
            raise Exception(
                'Unable to start multiprocessing group. The most likely ' +
                'reason is that the script is running with local_rank set ' +
                'but without the set-up for distributed operation. ' +
                'local_rank should be used only by torch.distributed.' +
                'launch. See the README.')
        device = helpers.set_torch_device(args.local_rank)
        # offset seeds so different GPUs get different data streams
        seed += args.local_rank
    else:
        device = helpers.set_torch_device(args.device)
    logs.main.write(helpers.formatted_env_info(device))

    # set random seed for this process
    np.random.seed(seed)
    torch.manual_seed(seed)
    if _MAKE_TORCH_DETERMINISTIC and device.type == 'cuda':
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

    return RESOURCE_INFO(is_multi_gpu, is_lead_process, device), logs


def load_data(args, log, res_info):
    log.write('* Loading data from {}\n'.format(args.input))
    log.write('* Per read file MD5 {}\n'.format(helpers.file_md5(args.input)))

    if args.input_strand_list is not None:
        read_ids = list(set(helpers.get_read_ids(args.input_strand_list)))
        log.write(('* Will train from a subset of {} strands, determined ' +
                   'by read_ids in input strand list\n').format(len(read_ids)))
    else:
        log.write('* Reads not filtered by id\n')
        read_ids = None


Complete the function implementation replacing "raise NotImplementedError".