class_docstr: Quantized counterpart of nn.Embedding module. Stores the centroids and
  the assignments. The full weight is re-instantiated at each forward
  pass.
  
  Args:
      - centroids: centroids of size n_centroids x block_size
      - assignments: assignments of the centroids to the subvectors
        of size self.out_features x n_blocks
      - bias: the non-quantized bias
  
  Remarks:
      - We refer the reader to the official documentation of the nn.Embedding module
        for the other arguments and the behavior of the module
      - Performance tests on GPU show that this implementation is 10% slower than
        the non-quantized nn.Embedding module for a standard training loop.
code: def forward(self, input):
      return F.embedding(
          input,
          self.weight,
          self.padding_idx,
          self.max_norm,
          self.norm_type,
          self.scale_grad_by_freq,
          self.sparse,
      )
  
file_path: fairseq/fairseq/modules/quantization/pq/modules/qemb.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 3 for calling the PQEmbedding obj's underlying member method code with example usage: obj.forward(input) and returns: F