class_docstr: Pooling by Multihead Attention from `Set Transformer: A Framework for Attention-based
  Permutation-Invariant Neural Networks <https://arxiv.org/abs/1810.00825>`__
  
  Parameters
  ----------
  k : int
      The number of seed vectors.
  d_model : int
      The feature size (input and output) in Multi-Head Attention layer.
  num_heads : int
      The number of heads.
  d_head : int
      The hidden size per head.
  d_ff : int
      The kernel size in FFN (Positionwise Feed-Forward Network) layer.
  dropouth : float
      The dropout rate of each sublayer.
  dropouta : float
      The dropout rate of attention heads.
  
  Notes
  -----
  This module was used in SetTransformer layer.
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docstr: Set the extra representation of the module.
  which will come into effect when printing the model.
docstr_tok: ["Set","the","extra","representation","of","the","module",".","which","will","come","into","effect","when","printing","the","model","."]
code: def extra_repr(self):
      """Set the extra representation of the module.
      which will come into effect when printing the model.
      """
      shape_str = "({}, {})".format(
          self.seed_vectors.shape[0], self.seed_vectors.shape[1]
      )
      return "SeedVector: " + shape_str
  
imports: import numpy
  import torch
  import torch.nn
  from ...backend import pytorch
  from ...base import dgl_warning
  from ...readout import broadcast_nodes, max_nodes, mean_nodes, softmax_nodes, sum_nodes, topk_nodes
label_desc: Use image node_id 4 for calling the PMALayer obj's underlying member method code with example usage: obj.extra_repr() and returns: unknown