class_docstr: Interpreter API implementation for dimensionality reduction model.
class_docstr_tok: ["Interpreter","API","implementation","for","dimensionality","reduction","model","."]
docstr: Convert inputs, preserving metadata for caching.
docstr_tok: ["Convert","inputs",",","preserving","metadata","for","caching","."]
code: def convert_input(
      self, inp: JsonDict, model_output: JsonDict
  ) -> JsonDict:
      """Convert inputs, preserving metadata for caching."""
      return {
          "x": model_output[self._field_name],
          "_id": inp.get("_id"),
      }
  
imports: from collections.abc import Hashable, Sequence
  import threading
  from typing import Optional
  from absl import logging
  from lit_nlp.api import components
  from lit_nlp.api import dataset
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import caching
label_desc: Use image node_id 3 for calling the ProjectionInterpreter obj's underlying member method code with example usage: obj.convert_input(inp, model_output) and returns: dict