class_docstr: Handler for `VarLenFeature` values.
  
  `VarLenFeature` values will be parsed as an array of the corresponding dtype.
class_docstr_tok: ["Handler","for","`","VarLenFeature","`","values",".","`","VarLenFeature","`","values","will","be","parsed","as","an","array","of","the","corresponding","dtype","."]
docstr: Initialize fields (performance caches) that point to example's state.
docstr_tok: ["Initialize","fields","(","performance","caches",")","that","point","to","example","'s","state","."]
code: def initialize_encode_cache(self, example):
      """Initialize fields (performance caches) that point to example's state."""
      self._cast_fn = _make_cast_fn(self._np_dtype)
      self._feature = example.features.feature[self._name]
      self._value = self._value_fn(self._feature)
  
file_path: transform/tensorflow_transform/coders/example_proto_coder.py
imports: import numpy
  import tensorflow
  from tensorflow_transform.tf_metadata import schema_utils
label_desc: Use image node_id 3 for calling the _VarLenFeatureHandler obj's underlying member method code with example usage: obj.initialize_encode_cache(example) without return types