class_docstr: Gaussian Conditional prior with mean and scale indexes
code: def _standardized_cumulative(self, inputs: Tensor) -> Tensor:
      half = float(0.5)
      const = float(-(2**-0.5))
      # Using the complementary error function maximizes numerical precision.
      return half * torch.erfc(const * inputs)
  
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 4 for calling the ContinuousIndexedGaussianConditional obj's underlying member method code with example usage: obj._standardized_cumulative(inputs) and returns: unknown