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def evaluate_ECE(data_iter, net, device, M=15):
    net.eval()

    bins, n = [0.0 for _ in range(M)], 0

    with torch.no_grad():
        for X_batch, y_batch in data_iter:
            p_batch = torch.exp(lsoftmax(net(X_batch.to(device))))
            y_hat_batch = net(X_batch.to(device)).argmax(dim=1)
            for p, y_hat, y in zip(p_batch.numpy(), y_hat_batch.numpy(), y_batch.numpy()):

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import torch

batch_size = 3
seq_len = 20
vocab_size = 4
emb_size = 6
conv_length = 2
num_conv_kernel = 30
channel_in = 1 # of course for text...

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import cv2
import numpy as np
import matplotlib
import matplotlib.pyplot as plt

output_size = 501
flow = np.zeros((output_size, output_size, 2), dtype=np.float32)

flow[:, :, 0] = np.arange(-(output_size // 2), output_size // 2 + 1)
flow[:, :, 1] = np.arange(-(output_size // 2), output_size // 2 + 1)[:, np.newaxis]

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import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import animation
from matplotlib.collections import PatchCollection

fig = plt.figure()
ax = plt.axes(xlim=(-1.5, 1.5), ylim=(-1.5, 1.5), aspect='equal')

# not visible

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import numpy as np
import matplotlib
import matplotlib.pyplot as plt

N = 10
T = 300
random_seed = 0

np.random.seed(random_seed)

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import svgpathtools
from svgpathtools import svg2paths2

paths, attributes, svg_attributes = svg2paths2('doubleset.svg')
SCALE = 10

print("\\begin{tikzpicture}")
for path in paths:
    print("\\begin{scope}")
    for b in path:

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