diff --git a/tensorflow_privacy/privacy/analysis/GDprivacy_accountants.py b/tensorflow_privacy/privacy/analysis/GDprivacy_accountants.py
deleted file mode 100644
index ba56dac..0000000
--- a/tensorflow_privacy/privacy/analysis/GDprivacy_accountants.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# =============================================================================
-
-r"""This code applies the Dual and Central Limit 
-Theorem (CLT) to estimate privacy budget of an iterated subsampled 
-Gaussian Mechanism (by either uniform or Poisson subsampling). 
-"""
-
-
-import numpy as np
-from scipy.stats import norm
-from scipy import optimize
-
-# Total number of examples:N
-# batch size:batch_size
-# Noise multiplier for DP-SGD/DP-Adam:noise_multiplier
-# current epoch:epoch
-# Target delta:delta
-
-# Compute mu from uniform subsampling
-def compute_muU(epoch,noise_multi,N,batch_size):
-    T=epoch*N/batch_size
-    c=batch_size*np.sqrt(T)/N
-    return(np.sqrt(2)*c*np.sqrt(np.exp(noise_multi**(-2))*norm.cdf(1.5/noise_multi)+3*norm.cdf(-0.5/noise_multi)-2))
-
-# Compute mu from Poisson subsampling
-def compute_muP(epoch,noise_multi,N,batch_size):
-    T=epoch*N/batch_size
-    return(np.sqrt(np.exp(noise_multi**(-2))-1)*np.sqrt(T)*batch_size/N)
-    
-# Dual between mu-GDP and (epsilon,delta)-DP
-def delta_eps_mu(eps,mu):
-    return norm.cdf(-eps/mu+mu/2)-np.exp(eps)*norm.cdf(-eps/mu-mu/2)
-
-# inverse Dual
-def eps_from_mu(mu,delta):
-    def f(x):
-        return delta_eps_mu(x,mu)-delta    
-    return optimize.root_scalar(f, bracket=[0, 500], method='brentq').root
-
-# inverse Dual of uniform subsampling
-def compute_epsU(epoch,noise_multi,N,batch_size,delta):
-    return(eps_from_mu(compute_muU(epoch,noise_multi,N,batch_size),delta))
-
-# inverse Dual of Poisson subsampling
-def compute_epsP(epoch,noise_multi,N,batch_size,delta):
-    return(eps_from_mu(compute_muP(epoch,noise_multi,N,batch_size),delta))
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