docstr: Test the RNN executor's handling of empty input sequences
docstr_tok: ["Test","the","RNN","executor","'s","handling","of","empty","input","sequences"]
code: def test_empty_sequence(self):
      """
      Test the RNN executor's handling of empty input sequences
      """
      Tseq = [0, 1, 2, 3, 0, 1]
      workspace.ResetWorkspace()
      with core.DeviceScope(caffe2_pb2.DeviceOption()):
          model, output = self.init_lstm_model(
              T=4, num_layers=1, forward_only=True, use_loss=False
          )
  
          workspace.RunNetOnce(model.param_init_net)
  
          self.enable_rnn_executor(model.net, 1, True)
  
          np.random.seed(10022015)
          first_call = True
          for seq_len in Tseq:
              input_shape = [seq_len, self.batch_size, self.input_dim]
              workspace.FeedBlob(
                  "input",
                  np.random.rand(*input_shape).astype(np.float32),
              )
              workspace.FeedBlob(
                  "target",
                  np.random.rand(
                      seq_len, self.batch_size, self.hidden_dim
                  ).astype(np.float32),
              )
              if first_call:
                  workspace.CreateNet(model.net, overwrite=True)
                  first_call = False
  
              workspace.RunNet(model.net.Proto().name)
              val = workspace.FetchBlob(output)
              self.assertEqual(val.shape[0], seq_len)
  
file_path: pytorch/caffe2/python/operator_test/recurrent_net_executor_test.py
imports: from caffe2.proto import caffe2_pb2
  from caffe2.python import model_helper, workspace, core, rnn_cell, test_util
  from caffe2.python.attention import AttentionType
  import numpy
  import unittest
  import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  from hypothesis import given, settings
label_desc: Use image node_id 4 for calling the TestRNNExecutor obj's underlying member method code with example usage: obj.test_empty_sequence() without return types