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 predict_step(
      self, batch: Any, batch_idx: int, dataloader_idx: int = 0
  ) -> Any:
      return super().predict_step(
          batch[DataKeys.INPUT],
          batch_idx,
          dataloader_idx=dataloader_idx,
      )
  
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 6 for calling the GraphEmbedder obj's underlying member method code with example usage: obj.predict_step(batch, batch_idx, dataloader_idx) and returns: super