class_docstr: This class represents a particular config for a set of signal
  processing based sparsification functions on tensors. This can
  be used both on weights, gradients and other tensors like the
  optimizer state.
  
  During initialization, this class requires a value for one of
  `sst_top_k_element` or `sst_top_k_percent` and also requires a
  value for one of `dst_top_k_element` or `dst_top_k_percent`.
  
  This class only handles tensor inputs and outputs. We leave
  state_dict type of data handling to upper layer functions.
  
  Args:
      algo (Algo):
          The algorithm used. Default: FFT
      sst_top_k_dim (int, optional):
          The dimension on which the top-k is done for SST.
          E.g. -1 is the last dim. None means flatten and top-k on all dims.
          There is no way to specify multiple dims other than None.
          Default: -1
      sst_top_k_element (int, optional):
          Number of top-k elements to retain for SST. Default: None
      sst_top_k_percent (float, optional):
          Percent of top-k elements to retain for SST. Default: None
      dst_top_k_dim (int, optional):
          The dimension on which the top-k is done for DST.
          E.g. -1 is the last dim. None means flatten and top-k on all dims.
          There is no way to specify multiple dims other than None.
          Default: None
      dst_top_k_element (int, optional):
          Number of top-k elements to retain for DST. Default: None
      dst_top_k_percent (float, optional):
          Percent of top-k elements to retain for DST. Default: None
  
  Example:
      .. code-block:: python
  
          2d_sparser = SignalSparsity(sst_top_k_element=10, dst_top_k_element=1)
          sst = 2d_sparser.dense_to_sst(linear.weight.data)
  
          3d_sparser = SingalSparsity(algo=Algo.FFT, sst_top_k_dim=None, dst_top_k_dim=-1, sst_top_k_percent=10, dst_top_k_element=100)
          conv.weight.data, _, _ = 3d_sparser.lossy_compress(conv.weight.data)
docstr: True if SST is enabled.
docstr_tok: ["True","if","SST","is","enabled","."]
code: def _sst_enabled(self) -> bool:
      """True if SST is enabled."""
      return (
          self._sst_top_k_element is not None
          or self._sst_top_k_percent is not None
      )
  
imports: from enum import Enum
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 2 for calling the SignalSparsity obj's underlying member method code with example usage: obj._sst_enabled() and returns: unknown