class_docstr: Caches fingerprints and outputs of nodes during model training.
class_docstr_tok: ["Caches","fingerprints","and","outputs","of","nodes","during","model","training","."]
docstr: Initializes a `TrainingHook`.
  
  Args:
      cache: Cache used to store fingerprints and outputs.
      model_storage: Used to cache `Resource`s.
      pruned_schema: The pruned training schema.
docstr_tok: ["Initializes","a","`","TrainingHook","`",".","Args",":","cache",":","Cache","used","to","store","fingerprints","and","outputs",".","model_storage",":","Used","to","cache","`","Resource","`","s",".","pruned_schema",":","The","pruned","training","schema","."]
code: def __init__(
      self,
      cache: TrainingCache,
      model_storage: ModelStorage,
      pruned_schema: GraphSchema,
  ) -> None:
      """Initializes a `TrainingHook`.
  
      Args:
          cache: Cache used to store fingerprints and outputs.
          model_storage: Used to cache `Resource`s.
          pruned_schema: The pruned training schema.
      """
      self._cache = cache
      self._model_storage = model_storage
      self._pruned_schema = pruned_schema
  
imports: import logging
  from typing import Any, Dict, Text, Type
  from rasa.engine.caching import TrainingCache
  from rasa.engine.graph import ExecutionContext, GraphNodeHook, GraphSchema, SchemaNode
  from rasa.engine.storage.storage import ModelStorage
  from rasa.engine.training.components import PrecomputedValueProvider
  import rasa.shared.utils.io
  from rasa.engine.training import fingerprinting
label_desc: Use image node_id 1 to create a new TrainingHook object from inherited base classes: GraphNodeHook with example: obj = TrainingHook(cache, model_storage, pruned_schema)