class_docstr: Quantizes incoming data using Gumbel-Softmax.
class_docstr_tok: ["Quantizes","incoming","data","using","Gumbel-Softmax","."]
docstr: Reset the parameters and buffers of the module.
docstr_tok: ["Reset","the","parameters","and","buffers","of","the","module","."]
code: def reset_parameters(self) -> None:
      """Reset the parameters and buffers of the module."""
      nn.init.uniform_(self.entries)
  
      self.num_updates.zero_()
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Optional, Tuple, final
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import Module, Parameter
  from torch.nn.functional import gumbel_softmax
  from fairseq2.nn.projection import Linear
  from fairseq2.typing import DataType, Device, finaloverride
label_desc: Use image node_id 2 for calling the GumbelVectorQuantizer obj's underlying member method code with example usage: obj.reset_parameters() without return types