class_docstr: The ``GraphEmbedder`` is a :class:`~flash.Task` for obtaining feature vectors (embeddings) from graphs. For more
  details, see :ref:`graph_embedder`.
  
  Args:
      backbone: A model to use to extract image features.
      pooling_fn: The global pooling operation to use (one of: "max", "max", "add" or a callable).
code: def load_from_checkpoint(
      cls,
      checkpoint_path: Union[str, IO],
      map_location: Optional[
          Union[Dict[str, str], str, torch.device, int, Callable]
      ] = None,
      hparams_file: Optional[str] = None,
      strict: bool = True,
      **kwargs,
  ) -> "GraphEmbedder":
      classifier = GraphClassifier.load_from_checkpoint(
          checkpoint_path,
          map_location=map_location,
          hparams_file=hparams_file,
          strict=strict,
          **kwargs,
      )
  
      return cls(classifier.backbone, classifier.pooling_fn)
  
file_path: lightning-flash/src/flash/graph/embedding/model.py
imports: from typing import IO, Any, Callable, Dict, Optional, Union
  import torch
  from torch import Tensor, nn
  from flash.core.data.io.input import DataKeys
  from flash.core.model import Task
  from flash.graph.classification.model import POOLING_FUNCTIONS, GraphClassifier
  from flash.graph.collate import _pyg_collate
label_desc: Use image node_id 7 for calling the GraphEmbedder obj's underlying member method code with example usage: obj.load_from_checkpoint(cls, checkpoint_path, map_location, hparams_file, strict) and returns: cls