code: def kl_div(logits, labels, reduction="mean"):
      """Computes the Kullback-Leibler (KL) Divergence between the logits and the
      labels.
  
      Parameters
      ----------
          logits (numpy array): The input logits array.
          labels (numpy array): The label array which has the same shape as logits.
          reduction (str): Specifies the reduction to be applied to the output.
                           Its value must be one of 'none', 'mean', 'batchmean',
                           or 'sum'. Default: 'mean'.
  
      Returns
      -------
          float or numpy array: If reduction is 'none', then output is
          a numpy array and has the same shape as logits.
                                Otherwise, it is a scalar (float).
      """
      assert ivy.shape(logits) == ivy.shape(
          labels
      ), "logits and labels must have the same shape."
      L = labels * (ivy.log(labels) - logits)
      if reduction == "none":
          return L
      elif reduction == "mean":
          return ivy.mean(L)
      elif reduction == "batchmean":
          return ivy.mean(L, axis=0)
      elif reduction == "sum":
          return ivy.sum(L)
      else:
          raise ValueError(
              "Invalid reduction mode. Supported values are 'none', 'mean', 'batchmean',"
              " or 'sum'."
          )
  
imports: import ivy
  from ivy.func_wrapper import with_supported_dtypes
  from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
  from ivy.functional.ivy.experimental.layers import _broadcast_pooling_helper
label_desc: Use image node_id 15 for calling a global function with example usage: kl_div(logits, labels, reduction) and returns: L, ivy, ivy, ivy