code: def build_char_reps(
      self,
      inputs,
      max_phrase_len,
      charlm,
      projection,
      begin_paddings,
      device,
  ):
      char_reps = charlm.build_char_representation(inputs)
      if projection is not None:
          char_reps = [projection(x) for x in char_reps]
      char_inputs = torch.zeros(
          (len(inputs), max_phrase_len, char_reps[0].shape[-1]),
          device=device,
      )
      for idx, rep in enumerate(char_reps):
          start = begin_paddings[idx]
          end = start + rep.shape[0]
          char_inputs[idx, start:end, :] = rep
      return char_inputs
  
imports: import logging
  import math
  import os
  import random
  import re
  from types import SimpleNamespace
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import stanza.models.classifiers.data
  from stanza.models.classifiers.base_classifier import BaseClassifier
  from stanza.models.classifiers.data import SentimentDatum
  from stanza.models.classifiers.utils import ExtraVectors, ModelType, build_output_layers
  from stanza.models.common.bert_embedding import extract_bert_embeddings
  from stanza.models.common.data import get_long_tensor, sort_all
  from stanza.models.common.foundation_cache import load_bert
  from stanza.models.common.vocab import PAD_ID, UNK_ID
label_desc: Use image node_id 6 for calling the CNNClassifier obj's underlying member method code with example usage: obj.build_char_reps(inputs, max_phrase_len, charlm, projection, begin_paddings, device) and returns: char_inputs