class_docstr: Coding `input - round(mean)` using `mean - round(mean)`
docstr: Decompress character strings to Tensors.
docstr_tok: ["Decompress","character","strings","to","Tensors","."]
code: def decompress(
      self, strings: str, scales: Tensor, means: Tensor
  ) -> Tensor:
      """
      Decompress character strings to Tensors.
      """
      indexes = self._get_indexes(means, scales)
      indexes = self._entropy_model._normalize_indexes(indexes)
      cdf_indexes = self._entropy_model.indexes_to_cdf_indexes(
          indexes_mean=indexes[..., 0], indexes_scale=indexes[..., 1]
      )  # Ints
      round_mean = torch.round(means)
      outputs = self._entropy_model.decompress(
          strings,
          cdf_indexes,  # type: ignore # expects IntTensor, whcih cdf_indexes is
      )
      return outputs + round_mean
  
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 7 for calling the GsnConditionalLocScaleShift obj's underlying member method code with example usage: obj.decompress(strings, scales, means) and returns: unknown