docstr: :param input_ids: Input data
  :param attention_maks: Attention mask value
  
  :return: output - Type of news for the given news snippet
docstr_tok: [":","param","input_ids",":","Input","data",":","param","attention_maks",":","Attention","mask","value",":","return",":","output","-","Type","of","news","for","the","given","news","snippet"]
code: def forward(self, input_ids, attention_mask):
      """
      :param input_ids: Input data
      :param attention_maks: Attention mask value
  
      :return: output - Type of news for the given news snippet
      """
      output = self.bert_model(
          input_ids=input_ids, attention_mask=attention_mask
      )
      output = F.relu(self.fc1(output.pooler_output))
      output = self.drop(output)
      output = self.out(output)
      return output
  
imports: import math
  import os
  import lightning
  import pandas
  import torch
  import torch.nn.functional
  import torchtext.datasets
  from lightning.pytorch.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint
  from lightning.pytorch.cli import LightningCLI
  from sklearn.datasets import fetch_20newsgroups
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from torch import nn
  from torch.utils.data import DataLoader
  from torch.utils.data.dataset import random_split
  from torchdata.datapipes.iter import IterDataPipe
  from torchtext.data.functional import to_map_style_dataset
  from torchtext.datasets import AG_NEWS
  from transformers import BertModel, BertTokenizer
  import mlflow.pytorch
label_desc: Use image node_id 2 for calling the BertNewsClassifier obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask) and returns: output