class_docstr: Forecaster base model for lstm, mtnet, seq2seq and tcn forecasters.
class_docstr_tok: ["Forecaster","base","model","for","lstm",",","mtnet",",","seq2seq","and","tcn","forecasters","."]
docstr: Save the onnx model file to the disk.
  
  :param dirname: The dir location you want to save the onnx file.
docstr_tok: ["Save","the","onnx","model","file","to","the","disk",".",":","param","dirname",":","The","dir","location","you","want","to","save","the","onnx","file","."]
code: def export_onnx_file(self, dirname):
      """
      Save the onnx model file to the disk.
  
      :param dirname: The dir location you want to save the onnx file.
      """
      if self.distributed:
          raise NotImplementedError(
              "export_onnx_file has not been supported for distributed\
                                         forecaster. You can call .to_local() to transform the\
                                         forecaster to a non-distributed version."
          )
      import torch
  
      dummy_input = torch.rand(
          1,
          self.data_config["past_seq_len"],
          self.data_config["input_feature_num"],
      )
      self.internal._build_onnx(dummy_input, dirname)
  
file_path: analytics-zoo/pyzoo/zoo/chronos/forecaster/base_forecaster.py
imports: from zoo.chronos.forecaster.abstract import Forecaster
  from zoo.orca.data.shard import SparkXShards
  from zoo.chronos.forecaster.utils import np_to_creator, set_pytorch_seed, check_data, xshard_to_np, np_to_xshard
  import numpy
  from zoo.orca.learn.pytorch.estimator import Estimator
  from zoo.orca.learn.metrics import MSE, MAE
label_desc: Use image node_id 12 for calling the BasePytorchForecaster obj's underlying member method code with example usage: obj.export_onnx_file(dirname) without return types