code: def get_auto_estimator(backend="torch"):
      loss = (
          "mse" if backend.startswith("keras") else torch.nn.MSELoss()
      )
      auto_seq2seq = AutoSeq2Seq(
          input_feature_num=input_feature_dim,
          output_target_num=output_feature_dim,
          past_seq_len=past_seq_len,
          future_seq_len=future_seq_len,
          optimizer="Adam",
          loss=loss,
          metric="mse",
          lr=hp.choice([0.001, 0.003, 0.01]),
          lstm_hidden_dim=hp.grid_search([32, 64, 128]),
          lstm_layer_num=hp.randint(1, 4),
          dropout=hp.uniform(0.1, 0.3),
          teacher_forcing=False,
          logs_dir="/tmp/auto_seq2seq",
          backend=backend,
          cpus_per_trial=2,
          name="auto_seq2seq",
      )
      return auto_seq2seq
  
imports: from bigdl.chronos.utils import LazyImport
  import numpy
  from unittest import TestCase
  import pytest
  import tempfile
  from ...None import op_torch, op_tf2, op_distributed, op_inference
label_desc: Use image node_id 5 for calling a global function with example usage: get_auto_estimator(backend) and returns: auto_seq2seq