docstr: :param chars: [B, max_num_chars]
  :return: [B, D]
docstr_tok: [":","param","chars",":","[","B",",","max_num_chars","]",":","return",":","[","B",",","D","]"]
code: def forward(self, chars):
      """
      :param chars: [B, max_num_chars]
      :return: [B, D]
      """
      cnn_input = nn.functional.one_hot(
          chars, self.__num_chars_in_vocabulary
      )  # [B , max_num_chars, char_vocab_size]
      cnn_input = cnn_input.transpose(1, 2).float()
      l1_out = self.__conv_l1(cnn_input)
      l2_out = self.__conv_l2(nn.functional.relu(l1_out))
      l3_out = self.__conv_l3(
          nn.functional.relu(l2_out)
      )  # [B, Dout, max_num_chars - pad]
  
      summary, _ = torch.max(l3_out, dim=-1)  # [B, D']
      return self.__dropout(summary)
  
imports: from typing_extensions import Final, Literal
  import logging
  import math
  import numpy
  import torch
  import torch.nn
  from dpu_utils.codeutils import split_identifier_into_parts
  from dpu_utils.mlutils import BpeVocabulary, CharTensorizer, Vocabulary
  from typing import Any, Counter, Dict, List, NamedTuple, Optional, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel
  from ptgnn.neuralmodels.gnn.structs import AbstractNodeEmbedder
label_desc: Use image node_id 2 for calling the CharUnitEmbedder obj's underlying member method code with example usage: obj.forward(chars) and returns: self