class_docstr: The Graphormer model is a graph-encoder model.
  
  It goes from a graph to its representation. If you want to use the model for a downstream classification task, use
  GraphormerForGraphClassification instead. For any other downstream task, feel free to add a new class, or combine
  this model with a downstream model of your choice, following the example in GraphormerForGraphClassification.
class_docstr_tok: ["The","Graphormer","model","is","a","graph-encoder","model",".","It","goes","from","a","graph","to","its","representation",".","If","you","want","to","use","the","model","for","a","downstream","classification","task",",","use","GraphormerForGraphClassification","instead",".","For","any","other","downstream","task",",","feel","free","to","add","a","new","class",",","or","combine","this","model","with","a","downstream","model","of","your","choice",",","following","the","example","in","GraphormerForGraphClassification","."]
docstr: Maximum output length supported by the encoder.
docstr_tok: ["Maximum","output","length","supported","by","the","encoder","."]
code: def max_nodes(self):
      """Maximum output length supported by the encoder."""
      return self.max_nodes
  
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 4 for calling the GraphormerModel obj's underlying member method code with example usage: obj.max_nodes() and returns: self