docstr: This function calculates the cross entropy loss between logits and labels.
  
  Parameters:
  logits: The predicted values.
  labels: The actual values.
  step_frac: The fraction of total training steps completed.
  
  Returns:
  The mean of the cross entropy loss.
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code: def __call__(
      self, logits: torch.Tensor, labels: torch.Tensor, step_frac: float
  ) -> torch.Tensor:
      """
      This function calculates the cross entropy loss between logits and labels.
  
      Parameters:
      logits: The predicted values.
      labels: The actual values.
      step_frac: The fraction of total training steps completed.
  
      Returns:
      The mean of the cross entropy loss.
      """
      loss = torch.nn.functional.cross_entropy(logits, labels)
      return loss.mean()
  
file_path: weak-to-strong/weak_to_strong/loss.py
imports: import torch
label_desc: Use image node_id 1 for calling the xent_loss obj's underlying member method code with example usage: obj.__call__(logits, labels, step_frac) and returns: loss