class_docstr: GestureRecognizer for building hand gesture recognizer model.
  
  Attributes:
    embedding_size: Size of the input gesture embedding vector.
docstr: Creates and trains the model.
  
  Args:
    train_data: Training data.
    validation_data: Validation data.
docstr_tok: ["Creates","and","trains","the","model",".","Args",":","train_data",":","Training","data",".","validation_data",":","Validation","data","."]
code: def _create_and_train_model(
      self,
      train_data: classification_ds.ClassificationDataset,
      validation_data: classification_ds.ClassificationDataset,
  ):
      """Creates and trains the model.
  
      Args:
        train_data: Training data.
        validation_data: Validation data.
      """
      self._create_model()
      self._train_model(
          train_data=train_data,
          validation_data=validation_data,
          checkpoint_path=self._get_checkpoint_path(),
      )
  
imports: import os
  from typing import List
  import tensorflow
  from mediapipe.model_maker.python.core.data import classification_dataset
  from mediapipe.model_maker.python.core.tasks import classifier
  from mediapipe.model_maker.python.core.utils import loss_functions
  from mediapipe.model_maker.python.core.utils import model_util
  from mediapipe.model_maker.python.vision.gesture_recognizer import constants
  from mediapipe.model_maker.python.vision.gesture_recognizer import gesture_recognizer_options
  from mediapipe.model_maker.python.vision.gesture_recognizer import hyperparameters
  from mediapipe.model_maker.python.vision.gesture_recognizer import metadata_writer
  from mediapipe.model_maker.python.vision.gesture_recognizer import model_options
  from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer
label_desc: Use image node_id 3 for calling the GestureRecognizer obj's underlying member method code with example usage: obj._create_and_train_model(train_data, validation_data) without return types