class_docstr: Gaussian Conditional prior with mean and scale indexes
code: def update_tables(self, force: bool = True) -> bool:
      # Check if we need to update the gaussian conditional parameters, the
      # offsets are only computed and stored when the conditonal model is updated
      if self._offset.numel() > 0 and not force:
          return False
      device = self._indexes_table.device
  
      indexes = [
          torch.arange(0, r, dtype=torch.int32, device=device)
          for r in self._index_ranges
      ]  # [means, scales]
      indexes = torch.meshgrid(*indexes, indexing="ij")
      indexes = torch.stack(indexes, dim=-1)  # [n_means, n_scales, 2]
      self._indexes_table = self._prepare_table(table=indexes).to(
          device
      )
  
      # Compute the mean and scale grids of the quantized prior
      _prior_mean, _prior_scale = self._get_prior_params(
          indexes[..., 0], indexes[..., 1]
      )
      # sanity check: grids are increaing
      assert _prior_mean[:, 0].equal(
          torch.sort(_prior_mean[:, 0]).values
      )
      assert _prior_scale[0].equal(torch.sort(_prior_scale[0]).values)
  
      self._prior_mean = _prior_mean
      self._prior_scale = _prior_scale
      self._build_tables()
  
      return True
  
imports: import math
  from typing import Any, List, Optional, Tuple
  import scipy.stats
  import torch
  import torch.nn
  from compressai.entropy_models import EntropyModel
  from compressai.ops import LowerBound
  from torch import Tensor
label_desc: Use image node_id 6 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj.update_tables(force) and returns: True, False