code: def setup_method(self):
      super().setup_method()
      self.vocab = Vocabulary()
      self.vocab.add_token_to_namespace("1", "token_characters")
      self.vocab.add_token_to_namespace("2", "token_characters")
      self.vocab.add_token_to_namespace("3", "token_characters")
      self.vocab.add_token_to_namespace("4", "token_characters")
      params = Params(
          {
              "embedding": {
                  "embedding_dim": 2,
                  "vocab_namespace": "token_characters",
              },
              "encoder": {
                  "type": "cnn",
                  "embedding_dim": 2,
                  "num_filters": 4,
                  "ngram_filter_sizes": [1, 2],
                  "output_dim": 3,
              },
          }
      )
      self.encoder = TokenCharactersEncoder.from_params(
          vocab=self.vocab, params=deepcopy(params)
      )
      self.embedding = Embedding.from_params(
          vocab=self.vocab, params=params["embedding"]
      )
      self.inner_encoder = Seq2VecEncoder.from_params(params["encoder"])
      constant_init = Initializer.from_params(
          Params({"type": "constant", "val": 1.0})
      )
      initializer = InitializerApplicator([(".*", constant_init)])
      initializer(self.encoder)
      initializer(self.embedding)
      initializer(self.inner_encoder)
  
file_path: allennlp/tests/modules/token_embedders/token_characters_encoder_test.py
imports: from copy import deepcopy
  import numpy
  from numpy.testing import assert_almost_equal
  import torch
  from allennlp.common import Params
  from allennlp.data import Vocabulary
  from allennlp.modules import Seq2VecEncoder
  from allennlp.modules.token_embedders import Embedding, TokenCharactersEncoder
  from allennlp.nn import InitializerApplicator, Initializer
  from allennlp.common.testing import AllenNlpTestCase
label_desc: Use image node_id 1 for calling the TestTokenCharactersEncoder obj's underlying member method code with example usage: obj.setup_method() without return types