class_docstr: Helper to scale `scales` before quantizing if to integer indices.
  
  Log quantization grid that aligns woth how scales are distributed
code: def __init__(
      self,
      scales_min: float = 0.01,
      scales_max: float = 256.0,
      num_bins: int = 256,
      verify_valid_scales: bool = True,
  ) -> None:
      self.scales_min = float(scales_min)
      self.scales_max = float(scales_max)
      self.num_bins = int(num_bins)
      self.verify_valid_scales = bool(verify_valid_scales)
  
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 1 to create a new Scaler object  with example: obj = Scaler(scales_min, scales_max, num_bins, verify_valid_scales)