code: def binary_cross_entropy_with_logits(
      logit,
      label,
      weight=None,
      reduction="mean",
      pos_weight=None,
      name=None,
  ):
      ret = ivy.binary_cross_entropy(
          label,
          logit,
          from_logits=True,
          reduction="none",
          pos_weight=pos_weight,
      )
      reduction = _get_reduction_func(reduction)
      if weight is not None:
          ret = ivy.multiply(weight, ret)
      ret = reduction(ret).astype(label.dtype)
      return paddle.to_tensor(ivy.atleast_1d(ret))
  
imports: import ivy
  from ivy.func_wrapper import with_supported_dtypes
  import ivy.functional.frontends.paddle
  from ivy.utils.exceptions import handle_exceptions
  from ivy.functional.frontends.paddle.func_wrapper import inputs_to_ivy_arrays, to_ivy_arrays_and_back
label_desc: Use image node_id 4 for calling a global function with example usage: binary_cross_entropy_with_logits(logit, label, weight, reduction, pos_weight, name) and returns: paddle