class_docstr: The linear scaled attention mechanism from the
  `"Rethinking Attention with Performers"
  <https://arxiv.org/abs/2009.14794>`_ paper.
  
  Args:
      channels (int): Size of each input sample.
      heads (int, optional): Number of parallel attention heads.
      head_channels (int, optional): Size of each attention head.
          (default: :obj:`64.`)
      kernel (Callable, optional): Kernels for generalized attention.
          If not specified, `ReLU` kernel will be used.
          (default: :obj:`torch.nn.ReLU()`)
      qkv_bias (bool, optional): If specified, add bias to query, key
          and value in the self attention. (default: :obj:`False`)
      attn_out_bias (bool, optional): If specified, add bias to the
          attention output. (default: :obj:`True`)
      dropout (float, optional): Dropout probability of the final
          attention output. (default: :obj:`0.0`)
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code: def _reset_parameters(self):
      self.q.reset_parameters()
      self.k.reset_parameters()
      self.v.reset_parameters()
      self.attn_out.reset_parameters()
      self.redraw_projection_matrix()
  
file_path: pytorch_geometric/torch_geometric/nn/attention/performer.py
imports: import math
  from typing import Callable, Optional
  import torch
  from torch import Tensor
label_desc: Use image node_id 4 for calling the PerformerAttention obj's underlying member method code with example usage: obj._reset_parameters() without return types