class_docstr: Still under development.
class_docstr_tok: ["Still","under","development","."]
code: def _reset_cache(self):
      if self._initialized == False:
          return
  
      _w_sum = []
      _xw_sum = []
  
      for n_categories in self.n_categories:
          _w_sum.append(
              torch.zeros(
                  *n_categories[:-1],
                  dtype=self.probs[0].dtype,
                  device=self.device,
              )
          )
          _xw_sum.append(
              torch.zeros(
                  *n_categories,
                  dtype=self.probs[0].dtype,
                  device=self.device,
              )
          )
  
      self._w_sum = BufferList(_w_sum)
      self._xw_sum = BufferList(_xw_sum)
  
      self._log_probs = BufferList(
          [torch.log(prob) for prob in self.probs]
      )
  
file_path: pomegranate/pomegranate/distributions/conditional_categorical.py
imports: import numpy
  import torch
  import itertools
  from .._utils import _cast_as_tensor
  from .._utils import _cast_as_parameter
  from .._utils import _update_parameter
  from .._utils import _check_parameter
  from .._utils import _reshape_weights
  from .._utils import BufferList
  from ._distribution import ConditionalDistribution
  from .categorical import Categorical
label_desc: Use image node_id 3 for calling the ConditionalCategorical obj's underlying member method code with example usage: obj._reset_cache() without return types