class_docstr: The Set Attention Block (SAB) from the `"Set Transformer: A
  Framework for Attention-based Permutation-Invariant Neural Networks"
  <https://arxiv.org/abs/1810.00825>`_ paper.
  
  .. math::
  
      \mathrm{SAB}(\mathbf{X}) = \mathrm{MAB}(\mathbf{x}, \mathbf{y})
  
  Args:
      channels (int): Size of each input sample.
      heads (int, optional): Number of multi-head-attentions.
          (default: :obj:`1`)
      norm (str, optional): If set to :obj:`False`, will not apply layer
          normalization. (default: :obj:`True`)
      dropout (float, optional): Dropout probability of attention weights.
          (default: :obj:`0`)
class_docstr_tok: ["The","Set","Attention","Block","(","SAB",")","from","the","`","``","Set","Transformer",":","A","Framework","for","Attention-based","Permutation-Invariant","Neural","Networks","''","<","https",":","\/\/arxiv.org\/abs\/1810.00825",">","`","_","paper",".","..","math",":",":","\\mathrm","{","SAB","}","(","\\mathbf","{","X","}",")","=","\\mathrm","{","MAB","}","(","\\mathbf","{","x","}",",","\\mathbf","{","y","}",")","Args",":","channels","(","int",")",":","Size","of","each","input","sample",".","heads","(","int",",","optional",")",":","Number","of","multi-head-attentions",".","(","default",":",":","obj",":","`","1","`",")","norm","(","str",",","optional",")",":","If","set","to",":","obj",":","`","False","`",",","will","not","apply","layer","normalization",".","(","default",":",":","obj",":","`","True","`",")","dropout","(","float",",","optional",")",":","Dropout","probability","of","attention","weights",".","(","default",":",":","obj",":","`","0","`",")"]
code: def reset_parameters(self):
      self.mab.reset_parameters()
  
imports: from typing import Optional
  import torch
  from torch import Tensor
  from torch.nn import LayerNorm, Linear, MultiheadAttention, Parameter
label_desc: Use image node_id 2 for calling the SetAttentionBlock obj's underlying member method code with example usage: obj.reset_parameters() without return types