code: def __init__(
      self,
      data: DatasetSplits,
      embedding_size: int,
      glove_path: str = None,
      padding_idx: int = 0,
  ):
      super(CBOWClassifier, self).__init__()
      self.num_embeddings = len(data.vectorizer.data_vocab)
      self.embedding_size = embedding_size
      self.padding_idx = padding_idx
  
      if glove_path:
          logger.info("Using pre-trained word embeddings...")
          self.embeddings = Embedding(
              glove_filepath=glove_path, data=data
          ).embeddings
          self.embeddings = torch.from_numpy(self.embeddings).float()
          glove_size = len(self.embeddings[0])
          self.embedding: torch.nn.Embedding = torch.nn.Embedding(
              embedding_dim=glove_size,
              num_embeddings=self.num_embeddings,
              padding_idx=self.padding_idx,
              _weight=self.embeddings,
          )
  
      else:
          logger.info("Not using pre-trained word embeddings...")
          self.embedding: torch.nn.Embedding = torch.nn.Embedding(
              embedding_dim=self.embedding_size,
              num_embeddings=self.num_embeddings,
              padding_idx=self.padding_idx,
          )
  
      self.fc1 = torch.nn.Linear(
          in_features=embedding_size, out_features=self.num_embeddings
      )
      self.dropout = torch.nn.Dropout(p=0.3)
  
imports: import logging
  from typing import Dict, List, Any
  import numpy
  import pandas
  import torch
  from transfer_nlp.common.tokenizers import CustomTokenizer
  from transfer_nlp.embeddings.embeddings import Embedding
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.loaders.vectorizers import Vectorizer
  from transfer_nlp.loaders.vocabulary import CBOWVocabulary
  from transfer_nlp.plugins.config import register_plugin
  from transfer_nlp.plugins.predictors import PredictorABC
label_desc: Use image node_id 1 to create a new CBOWClassifier object from inherited base classes: torch.nn with example: obj = CBOWClassifier(data, embedding_size, glove_path, padding_idx)