code: def homo_lr_loss(pred, labels, dim=1):
      """
      The function assumes that pred has shape (n, num_classes) where each class has its own linear model.
      labels have shape (n,) and the values are integers denoting the class.
      """
  
      # initialize the loss
      loss = 0.0
      loss_fn = t.nn.BCELoss()
      if dim <= 2:
          return loss_fn(pred[:, 0], labels)
  
      for c in range(dim):
          # get binary labels for this class
          binary_labels = (labels == c).float().flatten()
          bin_pred = pred[:, c].flatten()
          # compute binary cross-entropy loss
          loss = loss_fn(bin_pred, binary_labels)
      # normalize loss by the number of classes
      loss /= dim
  
      return loss
  
imports: import torch.nn
  from fate.arch.dataframe import DataFrame
  from fate.ml.abc.module import HomoModule
  from fate.ml.utils.model_io import ModelIO
  from fate.arch import Context
  import logging
  import torch
  from fate.ml.nn.homo.fedavg import FedAVGClient, TrainingArguments, FedAVGArguments
  from transformers import default_data_collator
  import functools
  import tempfile
  from fate.ml.utils.predict_tools import array_to_predict_df
  from fate.ml.utils.predict_tools import MULTI, BINARY
  from fate.ml.nn.dataset.table import TableDataset
  from fate.ml.utils._optimizer import optimizer_factory, lr_scheduler_factory
label_desc: Use image node_id 1 for calling a global function with example usage: homo_lr_loss(pred, labels, dim) and returns: loss, loss_fn