class_docstr: Quantize tensor based on min/max of tensor values with the fixed range.
class_docstr_tok: ["Quantize","tensor","based","on","min\/max","of","tensor","values","with","the","fixed","range","."]
docstr: Construct an FixedQuantizer.
  
  This is an experimental API not subject to backward compatibility.
  
  Args:
    num_bits: Number of bits for quantization
    init_min: the lower end of quantization interval.
    init_max: the upper end of quantization interval.
    narrow_range: In case of 8 bits, narrow_range nudges the quantized range
      to be [-127, 127] instead of [-128, 127]. This ensures symmetric
      range has 0 as the centre.
code: def __init__(self, num_bits, init_min, init_max, narrow_range):
      """Construct an FixedQuantizer.
  
      This is an experimental API not subject to backward compatibility.
  
      Args:
        num_bits: Number of bits for quantization
        init_min: the lower end of quantization interval.
        init_max: the upper end of quantization interval.
        narrow_range: In case of 8 bits, narrow_range nudges the quantized range
          to be [-127, 127] instead of [-128, 127]. This ensures symmetric
          range has 0 as the centre.
      """
      self.num_bits = num_bits
      self.init_min = init_min
      self.init_max = init_max
      self.narrow_range = narrow_range
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import six
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras import quant_ops
label_desc: Use image node_id 1 to create a new FixedQuantizer object from inherited base classes: _QuantizeHelper, Quantizer with example: obj = FixedQuantizer(num_bits, init_min, init_max, narrow_range)