code: def run(self, num_epochs=1000, model=None):
      # Compute exact solution with full rank matrix
      p = self.solve_discrete_lv(self.A)
      # To initialize model otherwise can feed in model and rerun
      if model is None:
          model = LowRankVectorEmbedding(self)
          model.collect_params().initialize(
              mx.init.Xavier(magnitude=2.24),
              force_reinit=True,
              ctx=self.ctx,
          )
      trainer = gluon.Trainer(model.collect_params(), "adam")
      p_approx = train(p, trainer, model, num_epochs)
      B = model.embedding_low_rank_mat_B(model.feat_static_cat).T
      C = (
          None
          if self.is_sym
          else model.embedding_low_rank_mat_C(model.feat_static_cat).T
      )
      A_approx = compute_low_rank_product(B, C)
      return p_approx, p, A_approx, model
  
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 3 for calling the NeuralLV obj's underlying member method code with example usage: obj.run(num_epochs, model) and returns: p_approx,  p,  A_approx,  model