class_docstr: PerStoreFeature extracts one feature vector per BufferStoreNode
  
  Parameters
  ----------
  buffers_per_store : int
      The number of buffers in each BufferStore; Pad or truncate if necessary.
  arith_intensity_curve_num_samples : int
      The number of samples used in the arithmetic intensity curve.
  cache_line_bytes : int
      The number of bytes in a cache line.
  extract_workload : bool
      Whether to extract features in the workload in tuning context or not.
class_docstr_tok: ["PerStoreFeature","extracts","one","feature","vector","per","BufferStoreNode","Parameters","--","--","--","--","--","buffers_per_store",":","int","The","number","of","buffers","in","each","BufferStore",";","Pad","or","truncate","if","necessary",".","arith_intensity_curve_num_samples",":","int","The","number","of","samples","used","in","the","arithmetic","intensity","curve",".","cache_line_bytes",":","int","The","number","of","bytes","in","a","cache","line",".","extract_workload",":","bool","Whether","to","extract","features","in","the","workload","in","tuning","context","or","not","."]
code: def __init__(
      self,
      buffers_per_store: int = 5,
      arith_intensity_curve_num_samples: int = 10,
      cache_line_bytes: int = 64,
      extract_workload: bool = False,
  ):
      self.__init_handle_by_constructor__(
          _ffi_api.FeatureExtractorPerStoreFeature,  # type: ignore # pylint: disable=no-member
          buffers_per_store,
          arith_intensity_curve_num_samples,
          cache_line_bytes,
          extract_workload,
      )
  
imports: from tvm._ffi import register_object
  from ..None import _ffi_api
  from .feature_extractor import FeatureExtractor
label_desc: Use image node_id 1 to create a new PerStoreFeature object from inherited base classes: FeatureExtractor with example: obj = PerStoreFeature(buffers_per_store, arith_intensity_curve_num_samples, cache_line_bytes, extract_workload)