class_docstr: Holds loaded intent and entity training data.
class_docstr_tok: ["Holds","loaded","intent","and","entity","training","data","."]
docstr: Ensures that the loaded training data is valid.
  
  Checks that the data has a minimum of certain training examples.
docstr_tok: ["Ensures","that","the","loaded","training","data","is","valid",".","Checks","that","the","data","has","a","minimum","of","certain","training","examples","."]
code: def validate(self) -> None:
      """Ensures that the loaded training data is valid.
  
      Checks that the data has a minimum of certain training examples.
      """
      logger.debug("Validating training data...")
      if "" in self.intents:
          rasa.shared.utils.io.raise_warning(
              "Found empty intent, please check your "
              "training data. This may result in wrong "
              "intent predictions."
          )
  
      if "" in self.responses:
          rasa.shared.utils.io.raise_warning(
              "Found empty response, please check your "
              "training data. This may result in wrong "
              "response predictions."
          )
  
      # emit warnings for intents with only a few training samples
      for intent, count in self.number_of_examples_per_intent.items():
          if count < self.MIN_EXAMPLES_PER_INTENT:
              rasa.shared.utils.io.raise_warning(
                  f"Intent '{intent}' has only {count} training examples! "
                  f"Minimum is {self.MIN_EXAMPLES_PER_INTENT}, training may fail."
              )
  
      # emit warnings for entities with only a few training samples
      for entity, count in self.number_of_examples_per_entity.items():
          if count < self.MIN_EXAMPLES_PER_ENTITY:
              rasa.shared.utils.io.raise_warning(
                  f"Entity {entity} has only {count} training examples! "
                  f"The minimum is {self.MIN_EXAMPLES_PER_ENTITY}, because of "
                  f"this the training may fail."
              )
  
      # emit warnings for response intents without a response template
      for example in self.training_examples:
          if example.get(INTENT_RESPONSE_KEY) and not example.get(
              RESPONSE
          ):
              rasa.shared.utils.io.raise_warning(
                  f"Your training data contains an example "
                  f"'{example.get(TEXT)[:20]}...' "
                  f"for the '{example.get_full_intent()}' intent. "
                  f"You either need to add a response phrase or correct the "
                  f"intent for this example in your training data. "
                  f"If you intend to use Response Selector in the pipeline, the "
                  f"training may fail."
              )
  
imports: import logging
  import os
  from pathlib import Path
  import random
  from collections import Counter, OrderedDict
  import copy
  from os.path import relpath
  from typing import Any, Dict, List, Optional, Set, Text, Tuple, Callable
  import operator
  import rasa.shared.data
  from rasa.shared.utils.common import lazy_property
  import rasa.shared.utils.io
  from rasa.shared.nlu.constants import RESPONSE, INTENT_RESPONSE_KEY, ENTITY_ATTRIBUTE_TYPE, ENTITY_ATTRIBUTE_GROUP, ENTITY_ATTRIBUTE_ROLE, NO_ENTITY_TAG, INTENT, ENTITIES, TEXT, ACTION_NAME
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.training_data import util
label_desc: Use image node_id 33 for calling the TrainingData obj's underlying member method code with example usage: obj.validate() without return types