class_docstr: Interpreter API implementation for dimensionality reduction model.
class_docstr_tok: ["Interpreter","API","implementation","for","dimensionality","reduction","model","."]
code: def __init__(
      self,
      model: lit_model.Model,
      inputs: Sequence[JsonDict],
      model_outputs: Optional[list[JsonDict]],
      projector: lit_model.ProjectorModel,
      field_name: str,
      name: str,
  ):
      self._projector = caching.CachingModelWrapper(
          projector, name=name
      )
      self._field_name = field_name
  
      # Train on the given examples
      self._run(model, inputs, model_outputs, do_fit=True)
  
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 1 to create a new ProjectionInterpreter object from inherited base classes: lit_components with example: obj = ProjectionInterpreter(model, inputs, model_outputs, projector, field_name, name)