docstr: Test various maxpool widths
  
  Also sets --filter_channels to a multiple of 2 but not of 3 for
  the test to make sure the math is done correctly on a non-divisible width
docstr_tok: ["Test","various","maxpool","widths","Also","sets","--","filter_channels","to","a","multiple","of","2","but","not","of","3","for","the","test","to","make","sure","the","math","is","done","correctly","on","a","non-divisible","width"]
code: def test_train_maxpool_width(
      self, tmp_path, fake_embeddings, train_file, dev_file
  ):
      """
      Test various maxpool widths
  
      Also sets --filter_channels to a multiple of 2 but not of 3 for
      the test to make sure the math is done correctly on a non-divisible width
      """
      args = [
          "--maxpool_width",
          "1",
          "--filter_channels",
          "20",
          "--bilstm_hidden_dim",
          "20",
      ]
      self.run_training(
          tmp_path, fake_embeddings, train_file, dev_file, args
      )
  
      args = [
          "--maxpool_width",
          "2",
          "--filter_channels",
          "20",
          "--bilstm_hidden_dim",
          "20",
      ]
      self.run_training(
          tmp_path, fake_embeddings, train_file, dev_file, args
      )
  
      args = [
          "--maxpool_width",
          "3",
          "--filter_channels",
          "20",
          "--bilstm_hidden_dim",
          "20",
      ]
      self.run_training(
          tmp_path, fake_embeddings, train_file, dev_file, args
      )
  
imports: import os
  import pytest
  import numpy
  import stanza.models.classifier
  import stanza.models.classifiers.data
  from stanza.models.classifiers.trainer import Trainer
  from stanza.models.common import pretrain
  from stanza.models.common import utils
  from stanza.tests.classifiers.test_data import train_file, dev_file, test_file, DATASET, SENTENCES
label_desc: Use image node_id 7 for calling the TestClassifier obj's underlying member method code with example usage: obj.test_train_maxpool_width(tmp_path, fake_embeddings, train_file, dev_file) without return types