code: def train(p, trainer, model, num_epochs=1000):
      loss = gluon.loss.L2Loss()
      tqdm_epochs = tqdm(range(num_epochs))
      for e in tqdm_epochs:
          with autograd.record():
              # forward pass
              p_approx = model.forward()
              Loss = loss(p, p_approx)
              Loss.backward()
          trainer.step(model.num_time_series)
          tqdm_epochs.set_postfix({"loss": nd.sum(Loss).asscalar()})
      return p_approx
  
file_path: gluonts/src/gluonts/nursery/auto_ode/auto_ode.py
imports: import numpy
  import matplotlib.pyplot
  import mxnet
  import mxnet.gluon.nn
  import mxnet.gluon.rnn
  from mxnet import gluon, nd, autograd
  from tqdm import tqdm_notebook
label_desc: Use image node_id 1 for calling a global function with example usage: train(p, trainer, model, num_epochs) and returns: p_approx