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=== l1_5e-5_quantizable_32_4_3_2 ===
    threshold = 0
        32 bits => 0.914 top5_acc
        4 bits => 0.9115 top5_acc
        3 bits => 0.9069 top5_acc
        2 bits => 0.8941 top5_acc
    threshold = 1e-06
        32 bits => 0.914 top5_acc
        4 bits => 0.05 top5_acc

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=== l1_5e-5_quantizable_32_4_3_2 ===
    threshold = 0
        32 bits => 0.6823 top1_acc
        4 bits => 0.6795 top1_acc
        3 bits => 0.6734 top1_acc
        2 bits => 0.6377 top1_acc
    threshold = 1e-06
        32 bits => 0.6823 top1_acc
        4 bits => 0.01 top1_acc
        3 bits => 0.01 top1_acc

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The following are for full precision (fp32) original resnset18:

cifar100 L2 1e-4 - top5 accuracy

unclamped => 0.77

threshold = 1e-08 => 0.77
threshold = 1e-07 => 0.77
threshold = 1e-06 => 0.77
threshold = 1e-05 => 0.77

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import os
from functools import partial
from typing import Any, Dict, List, Text

import kerastuner
import tensorflow as tf
import tensorflow.keras.backend as K
import tensorflow_transform as tft
import tensorflow_data_validation as tfdv
from absl import logging

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from typing import Dict, Text

import tensorflow as tf
from absl import logging
from tensorflow.keras.layers import (LSTM, Activation, Concatenate, Dense)
import kerastuner
from rnn.constants import (INPUT_FEATURE_KEYS, PREDICT_FEATURE_KEYS,
                           HP_HIDDEN_LATENT_DIM,
                           HP_HIDDEN_LAYER_NUM, HP_LR,
                           HP_PRE_OUTPUT_UNITS,

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import tensorflow as tf
import kerastuner

TRAIN_STEPS = 1000
EVAL_STEPS = 100

BATCH_SIZE = 128

INPUT_WINDOW_SIZE = 7
OUTPUT_WINDOW_SIZE = 1

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def get_pipeline(data_path: Text,
                 pyfiles_root: str = None, # parent directory for user modules
                 tune: bool = False, # do hparam tuning ?
                 hyper_params_uri: Text = None, # if not, provide some hparams uri
                 hyperparam_train_args: trainer_pb2.TrainArgs = None, 
                 hyperparam_eval_args: trainer_pb2.EvalArgs = None,
                 train_args: trainer_pb2.TrainArgs = None,
                 eval_args: trainer_pb2.EvalArgs = None,
                 push_args: Dict[Text, Any] = None):
    if not pyfiles_root:

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def transformed_name(key: Text) -> Text:
    return key + '_xf'
    
def gzip_reader_fn(filenames):
    return tf.data.TFRecordDataset(filenames, compression_type='GZIP')
  
def input_fn(file_pattern, tf_transform_output,
         feature_spec,
         # feature_keys, input_feature_keys, predict_feature_keys, or anything you like
         batch_size=256):

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def get_input_graph(input_feature_keys, input_window_size) -> Tuple[Input, tf.keras.layers.Layer]:
    transformed_columns = [transformed_name(
        key) for key in input_feature_keys]

    input_layers = {
        colname: Input(name=colname, shape=(
            input_window_size), dtype=tf.float32)
        for colname in transformed_columns
    }

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def _get_serve_tf_examples_fn(model, tf_transform_output):
  """Returns a function that parses a serialized tf.Example and applies TFT."""

  model.tft_layer = tf_transform_output.transform_features_layer()

  @tf.function
  def serve_tf_examples_fn(serialized_tf_examples):
    """Returns the output to be used in the serving signature."""
    feature_spec = tf_transform_output.raw_feature_spec()
    feature_spec.pop(_LABEL_KEY)