code: def __init__(self, config):
      super().__init__()
  
      self.config = config
  
      self.dense = torch.nn.Linear(
          config.hidden_size, config.hidden_size
      )
      self.dropout = torch.nn.Dropout(config.hidden_dropout_prob)
      self.out_proj = torch.nn.Linear(config.hidden_size, 1)
  
imports: from logging import getLogger
  from pathlib import Path
  from typing import List, Dict, Union, Optional, Tuple
  import numpy
  import torch
  from torch.nn import BCEWithLogitsLoss
  from transformers import AutoModelForSequenceClassification, AutoConfig, AutoModel, AutoTokenizer
  from transformers.modeling_outputs import SequenceClassifierOutput
  from deeppavlov.core.commands.utils import expand_path
  from deeppavlov.core.common.errors import ConfigError
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.torch_model import TorchModel
label_desc: Use image node_id 1 to create a new BinaryClassificationHead object from inherited base classes: torch.nn with example: obj = BinaryClassificationHead(config)