"""Storing the parameters of the coefs"""
import torch
import numpy as np


x_mean_32 = [-10049.90903,
             0.8560989,
             -46.52714291,
             5.571389768,
             55.08389728,
             -20.5797238,
             29.40314565,
             134.5995335,
             -1.007070439,
             4.49636793,
             15.55154488,
             0.976109885,
             1.545127982,
             0.287722519,
             4.564543711,
             -7.085755233,
             0.589879196,
             -0.225738201,
             -0.032648406,
             -0.918182078,
             0.011857805,
             -0.039005907,
             -0.688319705,
             6.330784758,
             -6.451238075,
             0.559648544,
             0.023355899,
             -0.807560087,
             6.91153944,
             -0.031815117,
             18.58189081,
             -0.787957543]

sqrt_var_32 = [3013.853847,
               2128.615854,
               2000.888508,
               1945.262748,
               1623.515527,
               1421.445933,
               1290.129839,
               1174.7333,
               1127.64108,
               925.8558175,
               920.2393366,
               857.048362,
               819.130631,
               800.3327695,
               742.8173561,
               732.1989946,
               714.3310284,
               652.0608919,
               647.0796875,
               599.5409841,
               592.4129418,
               588.2738648,
               585.5733706,
               547.6475506,
               537.5673561,
               499.8249073,
               495.3329091,
               487.1852817,
               483.0491914,
               475.5114148,
               458.28139,
               439.8448582]

x_mean_32 = torch.tensor(x_mean_32)
sqrt_var_32 = torch.tensor(sqrt_var_32)

x_mean_32_np = np.array(x_mean_32)
sqrt_var_32_np = np.array(sqrt_var_32)

"""Storing the parameters of the coefs"""
import torch
import numpy as np


x_mean_32 = [-10049.90903,
             0.8560989,
             -46.52714291,
             5.571389768,
             55.08389728,
             -20.5797238,
             29.40314565,
             134.5995335,
             -1.007070439,
             4.49636793,
             15.55154488,
             0.976109885,
             1.545127982,
             0.287722519,
             4.564543711,
             -7.085755233,
             0.589879196,
             -0.225738201,
             -0.032648406,
             -0.918182078,
             0.011857805,
             -0.039005907,
             -0.688319705,
             6.330784758,
             -6.451238075,
             0.559648544,
             0.023355899,
             -0.807560087,
             6.91153944,
             -0.031815117,
             18.58189081,
             -0.787957543]

sqrt_var_32 = [3013.853847,
               2128.615854,
               2000.888508,
               1945.262748,
               1623.515527,
               1421.445933,
               1290.129839,
               1174.7333,
               1127.64108,
               925.8558175,
               920.2393366,
               857.048362,
               819.130631,
               800.3327695,
               742.8173561,
               732.1989946,
               714.3310284,
               652.0608919,
               647.0796875,
               599.5409841,
               592.4129418,
               588.2738648,
               585.5733706,
               547.6475506,
               537.5673561,
               499.8249073,
               495.3329091,
               487.1852817,
               483.0491914,
               475.5114148,
               458.28139,
               439.8448582]

x_mean_32 = torch.tensor(x_mean_32)
sqrt_var_32 = torch.tensor(sqrt_var_32)

x_mean_32_np = np.array(x_mean_32)
sqrt_var_32_np = np.array(sqrt_var_32)

Storing the parameters of the coefs