code: def information_content(
      probabilities: Tensor, reduction: str = "sum", base: float = 2.0
  ) -> Tensor:
      r"""
      Estimate information content given a set of probabilities.
  
      The information content is estimated using Shannon's source coding theorem,
      i.e., :math:`-\sum_{i}^N log(p_i)`, where :math:`p_i` is the :math:`i`th
      index of ``probabilities``. With logarithm base-2, this function estimates
      the number of bits output by an entropy coder.
  
      Note:
          The expected value of the information content over a distribution is
          the entropy.
  
      Args:
          probabilities: A tensor of probability estimates.
          reduction: Type of reduction to apply. If ``"sum"``, applies the
              summation across all batch elements. If ``"batch_el"``, applies
              the sum for each batch element. If ``"none"``, this function simply
              applies the logarithm in the specified base.
          base: Logarithm base.
  
      Returns
          Estimate of information content.
      """
      if reduction not in ("sum", "batch_el", "none"):
          raise ValueError(
              f"Unrecognized reduction {reduction}, must be 'sum', 'batch_el', or 'none'."
          )
  
      if reduction == "sum":
          natural = torch.sum(torch.log(probabilities))
      elif reduction == "batch_el":
          natural = (
              torch.log(probabilities)
              .view(probabilities.shape[0], -1)
              .sum(dim=-1)
          )
      elif reduction == "none":
          natural = torch.log(probabilities)
  
      return (-1 / math.log(base)) * natural
  
label_desc: Use image node_id 1 for calling a global function with example usage: information_content(probabilities, reduction, base) and returns: unknown