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diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
deleted file mode 100644
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--- a/examples/plot_gromov.py
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-# -*- coding: utf-8 -*-
-"""
-==========================
-Gromov-Wasserstein example
-==========================
-
-This example is designed to show how to use the Gromov-Wassertsein distance
-computation in POT.
-"""
-
-# Author: Erwan Vautier <erwan.vautier@gmail.com>
-#         Nicolas Courty <ncourty@irisa.fr>
-#
-# License: MIT License
-
-import scipy as sp
-import numpy as np
-import matplotlib.pylab as pl
-from mpl_toolkits.mplot3d import Axes3D  # noqa
-import ot
-
-#############################################################################
-#
-# Sample two Gaussian distributions (2D and 3D)
-# ---------------------------------------------
-#
-# The Gromov-Wasserstein distance allows to compute distances with samples that
-# do not belong to the same metric space. For demonstration purpose, we sample
-# two Gaussian distributions in 2- and 3-dimensional spaces.
-
-
-n_samples = 30  # nb samples
-
-mu_s = np.array([0, 0])
-cov_s = np.array([[1, 0], [0, 1]])
-
-mu_t = np.array([4, 4, 4])
-cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
-
-
-xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)
-P = sp.linalg.sqrtm(cov_t)
-xt = np.random.randn(n_samples, 3).dot(P) + mu_t
-
-#############################################################################
-#
-# Plotting the distributions
-# --------------------------
-
-
-fig = pl.figure()
-ax1 = fig.add_subplot(121)
-ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')
-ax2 = fig.add_subplot(122, projection='3d')
-ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')
-pl.show()
-
-#############################################################################
-#
-# Compute distance kernels, normalize them and then display
-# ---------------------------------------------------------
-
-
-C1 = sp.spatial.distance.cdist(xs, xs)
-C2 = sp.spatial.distance.cdist(xt, xt)
-
-C1 /= C1.max()
-C2 /= C2.max()
-
-pl.figure()
-pl.subplot(121)
-pl.imshow(C1)
-pl.subplot(122)
-pl.imshow(C2)
-pl.show()
-
-#############################################################################
-#
-# Compute Gromov-Wasserstein plans and distance
-# ---------------------------------------------
-
-p = ot.unif(n_samples)
-q = ot.unif(n_samples)
-
-gw0, log0 = ot.gromov.gromov_wasserstein(
-    C1, C2, p, q, 'square_loss', verbose=True, log=True)
-
-gw, log = ot.gromov.entropic_gromov_wasserstein(
-    C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)
-
-
-print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))
-print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))
-
-
-pl.figure(1, (10, 5))
-
-pl.subplot(1, 2, 1)
-pl.imshow(gw0, cmap='jet')
-pl.title('Gromov Wasserstein')
-
-pl.subplot(1, 2, 2)
-pl.imshow(gw, cmap='jet')
-pl.title('Entropic Gromov Wasserstein')
-
-pl.show()