class_docstr: Saves layer histogram in a dict with layer names as keys and lists of NDArrays as
  values. The collected histogram will be used for calculating the optimal thresholds for
  quantization using KL divergence.
class_docstr_tok: ["Saves","layer","histogram","in","a","dict","with","layer","names","as","keys","and","lists","of","NDArrays","as","values",".","The","collected","histogram","will","be","used","for","calculating","the","optimal","thresholds","for","quantization","using","KL","divergence","."]
code: def post_collect(self):
      min_max_dict = self.get_optimal_thresholds(
          self.hist_dict, self.quantized_dtype, logger=self.logger
      )
      return min_max_dict
  
imports: import abc
  import ctypes
  import logging
  import os
  import warnings
  import numpy
  import mxnet
  from ..base import _LIB, check_call, py_str
  from ..base import c_array, c_str, mx_uint, mx_real_t, c_str_array
  from ..base import SymbolHandle
  from ..symbol import Symbol
  from ..None import ndarray
  from ..io import DataDesc
  from ..device import cpu, Device
  from ..util import is_np_array, wrap_ctx_to_device_func
label_desc: Use image node_id 3 for calling the _LayerHistogramCollector obj's underlying member method code with example usage: obj.post_collect() and returns: min_max_dict