docstr: :param x_in: input data tensor. x_in.shape should be (batch, input_dim)
  :param apply_softmax: flag for the softmax activation
          should be false if used with the Cross Entropy losses
  :return: the resulting tensor. tensor.shape should be (batch, output_dim)
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code: def forward(self, x_in: torch.Tensor) -> torch.Tensor:
      """
  
      :param x_in: input data tensor. x_in.shape should be (batch, input_dim)
      :param apply_softmax: flag for the softmax activation
              should be false if used with the Cross Entropy losses
      :return: the resulting tensor. tensor.shape should be (batch, output_dim)
      """
  
      x_embedded_sum = self.dropout(self.embedding(x_in).sum(dim=1))
      y_out = self.fc1(x_embedded_sum)
  
      return y_out
  
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 2 for calling the CBOWClassifier obj's underlying member method code with example usage: obj.forward(x_in) and returns: y_out