class_docstr: High-level wrapper for GPU embedding cache
docstr: Queries the GPU cache.
  
  Parameters
  ----------
  keys : Tensor
      The keys to query the GPU cache with.
  
  Returns
  -------
  tuple(Tensor, Tensor, Tensor)
      A tuple containing (values, missing_indices, missing_keys) where
      values[missing_indices] corresponds to cache misses that should be
      filled by quering another source with missing_keys.
docstr_tok: ["Queries","the","GPU","cache",".","Parameters","--","--","--","--","--","keys",":","Tensor","The","keys","to","query","the","GPU","cache","with",".","Returns","--","--","--","-","tuple","(","Tensor",",","Tensor",",","Tensor",")","A","tuple","containing","(","values",",","missing_indices",",","missing_keys",")","where","values","[","missing_indices","]","corresponds","to","cache","misses","that","should","be","filled","by","quering","another","source","with","missing_keys","."]
code: def query(self, keys):
      """Queries the GPU cache.
  
      Parameters
      ----------
      keys : Tensor
          The keys to query the GPU cache with.
  
      Returns
      -------
      tuple(Tensor, Tensor, Tensor)
          A tuple containing (values, missing_indices, missing_keys) where
          values[missing_indices] corresponds to cache misses that should be
          filled by quering another source with missing_keys.
      """
      self.total_queries += keys.shape[0]
      keys = F.astype(keys, self.idtype)
      values, missing_index, missing_keys = _CAPI_DGLGpuCacheQuery(
          self._cache, F.to_dgl_nd(keys)
      )
      self.total_miss += missing_keys.shape[0]
      return (
          F.from_dgl_nd(values),
          F.from_dgl_nd(missing_index),
          F.from_dgl_nd(missing_keys),
      )
  
file_path: dgl/python/dgl/cuda/gpu_cache.py
imports: from ..None import backend
  from .._ffi.function import _init_api
label_desc: Use image node_id 2 for calling the GPUCache obj's underlying member method code with example usage: obj.query(keys) and returns: F,  F,  F