code: def _build(
      self,
      num_classes: Optional[int] = None,
      labels: Optional[List[str]] = None,
      loss_fn: Optional[Callable] = None,
      metrics: Union[Metric, Mapping, Sequence, None] = None,
      multi_label: bool = False,
  ):
      self.num_classes = num_classes
      self.multi_label = multi_label
      self.labels = labels
  
      if metrics is None:
          metrics = (
              F1Score(
                  num_labels=num_classes, task="multilabel", top_k=1
              )
              if (multi_label and num_classes)
              else Accuracy()
          )
  
      if loss_fn is None:
          loss_fn = (
              binary_cross_entropy_with_logits
              if multi_label
              else F.cross_entropy
          )
  
      return metrics, loss_fn
  
imports: from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Union
  import torch
  import torch.nn.functional
  from pytorch_lightning.utilities import rank_zero_warn
  from torch import Tensor
  from torchmetrics import Accuracy, F1Score, Metric
  from flash.core.adapter import AdapterTask
  from flash.core.data.io.input import DataKeys
  from flash.core.data.io.output import Output
  from flash.core.model import Task
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, lazy_import, requires
  from flash.core.utilities.providers import _FIFTYONE
label_desc: Use image node_id 1 for calling the ClassificationMixin obj's underlying member method code with example usage: obj._build(num_classes, labels, loss_fn, metrics, multi_label) and returns: metrics,  loss_fn