code: def bi_tempered_logistic_loss(
      activations,
      labels,
      t1,
      t2,
      label_smoothing=0.0,
      num_iters=5,
      reduction="mean",
  ):
      """Bi-Tempered Logistic Loss.
      Args:
        activations: A multi-dimensional tensor with last dimension `num_classes`.
        labels: A tensor with shape and dtype as activations (onehot),
          or a long tensor of one dimension less than activations (pytorch standard)
        t1: Temperature 1 (< 1.0 for boundedness).
        t2: Temperature 2 (> 1.0 for tail heaviness, < 1.0 for finite support).
        label_smoothing: Label smoothing parameter between [0, 1). Default 0.0.
        num_iters: Number of iterations to run the method. Default 5.
        reduction: ``'none'`` | ``'mean'`` | ``'sum'``. Default ``'mean'``.
          ``'none'``: No reduction is applied, return shape is shape of
          activations without the last dimension.
          ``'mean'``: Loss is averaged over minibatch. Return shape (1,)
          ``'sum'``: Loss is summed over minibatch. Return shape (1,)
      Returns:
        A loss tensor.
      """
      if len(labels.shape) < len(activations.shape):  # not one-hot
          labels_onehot = torch.zeros_like(activations)
          labels_onehot.scatter_(1, labels[..., None], 1)
      else:
          labels_onehot = labels
  
      if label_smoothing > 0:
          num_classes = labels_onehot.shape[-1]
          labels_onehot = (
              1 - label_smoothing * num_classes / (num_classes - 1)
          ) * labels_onehot + label_smoothing / (num_classes - 1)
  
      probabilities = tempered_softmax(activations, t2, num_iters)
  
      loss_values = (
          labels_onehot * log_t(labels_onehot + 1e-10, t1)
          - labels_onehot * log_t(probabilities, t1)
          - labels_onehot.pow(2.0 - t1) / (2.0 - t1)
          + probabilities.pow(2.0 - t1) / (2.0 - t1)
      )
      loss_values = loss_values.sum(dim=-1)  # sum over classes
  
      if reduction == "none":
          return loss_values
      if reduction == "sum":
          return loss_values.sum()
      if reduction == "mean":
          return loss_values.mean()
  
imports: from typing import Optional
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 7 for calling a global function with example usage: bi_tempered_logistic_loss(activations, labels, t1, t2, label_smoothing, num_iters, reduction) and returns: loss_values, loss_values, loss_values