class_docstr: The abstract base class that represents a custom TensorFlow model.
class_docstr_tok: ["The","abstract","base","class","that","represents","a","custom","TensorFlow","model","."]
docstr: Initializes a custom model with model specs and other parameters.
  
  Args:
    model_spec: Specification for the model.
    shuffle: Whether the training data need be shuffled.
docstr_tok: ["Initializes","a","custom","model","with","model","specs","and","other","parameters",".","Args",":","model_spec",":","Specification","for","the","model",".","shuffle",":","Whether","the","training","data","need","be","shuffled","."]
code: def __init__(self, model_spec: Any, shuffle: bool):
      """Initializes a custom model with model specs and other parameters.
  
      Args:
        model_spec: Specification for the model.
        shuffle: Whether the training data need be shuffled.
      """
      self._model_spec = model_spec
      self._shuffle = shuffle
      self._preprocess = None
      self._model = None
  
file_path: mediapipe/mediapipe/model_maker/python/core/tasks/custom_model.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import os
  from typing import Any, Callable, Optional
  import tensorflow
  from mediapipe.model_maker.python.core.data import dataset
  from mediapipe.model_maker.python.core.utils import model_util
  from mediapipe.model_maker.python.core.utils import quantization
label_desc: Use image node_id 1 to create a new CustomModel object from inherited base classes: abc with example: obj = CustomModel(model_spec, shuffle)