class_docstr: Still under development.
class_docstr_tok: ["Still","under","development","."]
code: def from_summaries(self):
      if self.frozen == True:
          return
  
      for i in range(self.d):
          probs = self._xw_sum[i] / self._w_sum[i].unsqueeze(-1)
          probs = torch.nan_to_num(probs, 1.0 / probs.shape[-1])
  
          _update_parameter(self.probs[i], probs, self.inertia)
  
      self._reset_cache()
  
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 7 for calling the ConditionalCategorical obj's underlying member method code with example usage: obj.from_summaries() without return types