code: def initialize_kimcnn_model(self, sub_train):
      vocab_size = len(sub_train.vocab)
  
      embed_dim = self.embedding_matrix.shape[1]
      self.model = KimCNN(
          vocab_size,
          self.max_seq_len,
          num_classes=self.num_classes,
          dropout=self.dropout,
          out_channels=self.out_channels,
          embedding_matrix=self.embedding_matrix,
          embed_dim=embed_dim,
          freeze_embedding_layer=False,
          padding_idx=self.padding_idx,
          kernel_heights=self.kernel_heights,
      )
  
imports: import datetime
  import logging
  import tempfile
  import numpy
  from functools import partial
  from sklearn.preprocessing import MultiLabelBinarizer
  from small_text.classifiers.classification import EmbeddingMixin
  from small_text.integrations.pytorch.classifiers.base import PytorchClassifier
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.integrations.pytorch.models.kimcnn import KimCNN
  from small_text.utils.classification import get_splits
  from small_text.utils.context import build_pbar_context
  from small_text.utils.data import check_training_data, list_length
  from small_text.utils.annotations import early_stopping_deprecation_warning
  from small_text.utils.labels import csr_to_list, get_num_labels
  from small_text.utils.datetime import format_timedelta
  from small_text.utils.logging import verbosity_logger, VERBOSITY_MORE_VERBOSE
  from small_text.utils.system import get_tmp_dir_base
label_desc: Use image node_id 4 for calling the KimCNNClassifier obj's underlying member method code with example usage: obj.initialize_kimcnn_model(sub_train) without return types