code: def _is_sparsity_zero(
      dense: Tensor,
      topk_percent: Optional[float],
      topk_element: Optional[int],
      top_k_dim: Optional[int],
  ) -> bool:
      """Returns True when a given value of topk_percent or topk_element along a particular top_k_dim
      for an input tensor results in sparsity=0% (or top-100-percent). Otherwise, returns False.
      """
      if topk_percent is None and topk_element is None:
          return False  # 100% sparse
  
      top_k_total_size = _top_k_total_size(dense, top_k_dim)
      k = _get_k_for_topk(topk_percent, topk_element, top_k_total_size)
      return k == top_k_total_size
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 4 for calling a global function with example usage: _is_sparsity_zero(dense, topk_percent, topk_element, top_k_dim) and returns: unknown, False