class_docstr: Quantization bottleneck.
  
  This class quantizes its input based on a codebook. Each entry is quantized
  to its nearest codebook representation according to Euclidean distance.
  
  Args:
      codebook_sizes: Size of the codebook for each quantization level.
      hidden_dims: Hidden dimension size, i.e., the sizes of the input
          vectors.
      init_distribution: Initialization distribution for the codebooks.
      normalize_codebook: Whether to renormalize the codebook prior to each
          quantization run.
code: def reset_hits(self):
      for codebook_hits in self.named_buffers(prefix="codebook_hits"):
          codebook_hits *= 0
  
file_path: NeuralCompression/neuralcompression/layers/_q_bottleneck.py
imports: from typing import NamedTuple, Optional, Sequence
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
label_desc: Use image node_id 2 for calling the QBottleneck obj's underlying member method code with example usage: obj.reset_hits() without return types