code: def __init__(self, embedding_dim: int, num_classes: int):
      super().__init__()
      """num_classes should be 1 for regression, or the number of classes for classification"""
      self.lm_output_learned_bias = nn.Parameter(torch.zeros(1))
      self.classifier = nn.Linear(
          embedding_dim, num_classes, bias=False
      )
      self.num_classes = num_classes
  
imports: import math
  from typing import Iterable, Iterator, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithNoAttention, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_graphormer import GraphormerConfig
label_desc: Use image node_id 1 to create a new GraphormerDecoderHead object from inherited base classes: nn with example: obj = GraphormerDecoderHead(embedding_dim, num_classes)