code: 
  import numpy as np
  import torch
  import matplotlib.pyplot as plt
  from neurodiffeq import diff  # the differentiation operation
  from neurodiffeq.conditions import IVP  # the initial condition
  from neurodiffeq.networks import FCNN  # fully-connect neural network
  from neurodiffeq.solvers import Solver1D
  from neurodiffeq.callbacks import WeightCallback
  from neurodiffeq.callbacks import WeightCallback1, SolutionCallback, SaddleCallback
  from neurodiffeq.callbacks import PeriodLocal
  from sklearn.metrics import mean_squared_error
  # from sklearn.metrics.pairwise import cosine_similarity
  import copy
  import matplotlib.pyplot as plt
  
  DIFFEQS_TRAIN = {
      'exp': lambda u, t: [diff(u, t) + u],
      'exp1': lambda u, t: [diff(u, t) - u],
      'tanh': lambda u, t: [diff(u, t) + u ** 2 - 1],
      'psig': lambda u, t: [diff(u, t) - 3 * u + u ** 2],
      'r1': lambda u, t: [diff(u, t) - u + u ** 2 + u ** 3],
      'r2': lambda u, t: [diff(u, t) + u + u ** 2],
      'r3': lambda u, t: [diff(u, t) + u ** 2],
      'r4': lambda u, t: [diff(u, t) - u ** 2],
      'q1': lambda u, t: [diff(u, t) - u + u ** 2],
      'q2': lambda u, t: [diff(u, t) - u + u ** 2 - u ** 3],
      'q3': lambda u, t: [diff(u, t) + u ** 2 + u ** 4],
      'q4': lambda u, t: [diff(u, t) - u ** 2 - u ** 4],
      'high_order1': lambda u, t: [diff(u, t) + u - u ** 2 + u ** 3 - u ** 4 + u ** 5],
      'high_order2': lambda u, t: [diff(u, t) - u + u ** 2 - u ** 3 + u ** 4 - u ** 5],
      'baseline': lambda u, t: [diff(u,t)]
  }
  
  
  solsa = np.load('data/q3_train_solution/3000.npy')
  solsb = np.load('data/baseline_train_solution/3000.npy')
  analytical =np.load('data/q3_gt_test_solution/3000.npy')
  # pre1 =np.load('data/q2_q2_pretrain_500_solution/500.npy')
  # pre2 =np.load('data/baseline_q2_pretrain_500_solution/500.npy')
  
  plt.figure()
  plt.plot(solsa,label='q2')
  plt.plot(solsb,label='high_order_2')
  plt.plot(analytical,label='analytical_q2')
  # plt.plot(pre1,label='pre_q2_q2')
  # plt.plot(pre2,label='pre_baseline_q2')
  plt.legend()
  plt.show()
apis: numpy.load matplotlib.pyplot.legend matplotlib.pyplot.figure matplotlib.pyplot.show matplotlib.pyplot.plot