class_docstr: The sparsity param of each data group is multiplied by gamma every step_size epochs.
      
class_docstr_tok: ["The","sparsity","param","of","each","data","group","is","multiplied","by","gamma","every","step_size","epochs","."]
code: def get_schedule_param(self):
      if not self._get_sp_called_within_step:
          warnings.warn(
              "To get the last learning rate computed by the scheduler, "
              "please use `get_last_lr()`.",
              UserWarning,
          )
      data_groups = self.data_sparsifier.data_groups
      if (self.last_epoch == 0) or (
          self.last_epoch % self.step_size != 0
      ):
          return {
              name: config[self.schedule_param]
              for name, config in data_groups.items()
          }
  
      return {
          name: config[self.schedule_param] * self.gamma
          for name, config in data_groups.items()
      }
  
imports: from torch.ao.pruning._experimental.data_sparsifier.data_norm_sparsifier import DataNormSparsifier
  from torch.ao.pruning._experimental.data_scheduler.base_data_scheduler import BaseDataScheduler
  import torch
  import torch.nn
  from typing import List
  from torch.ao.pruning._experimental.data_sparsifier.lightning.callbacks.data_sparsity import PostTrainingDataSparsity, TrainingAwareDataSparsity
  from torch.ao.pruning._experimental.data_sparsifier.lightning.callbacks._data_sparstity_utils import _get_valid_name
  from torch.ao.pruning._experimental.data_sparsifier.base_data_sparsifier import SUPPORTED_TYPES
  from torch.testing._internal.common_utils import TestCase
  from torch.testing._internal.common_utils import run_tests
  import importlib
  import unittest
  import warnings
  import math
  from torch.nn.utils.parametrize import is_parametrized
label_desc: Use image node_id 2 for calling the StepSLScheduler obj's underlying member method code with example usage: obj.get_schedule_param() and returns: unknown, unknown