class_docstr: Abstract base class for deep learning time series classifiers.
  
  The base classifier provides a deep learning default method for
  _predict and _predict_proba, and provides a new abstract method for building a
  model.
  
  Parameters
  ----------
  batch_size : int, default = 40
      training batch size for the model
  
  Attributes
  ----------
  self.model_ - the fitted DL model
docstr: Load object from file location.
  
  Parameters
  ----------
  serial : Name of the zip file.
  
  Returns
  -------
  deserialized self resulting in output at `path`, of `cls.save(path)`
docstr_tok: ["Load","object","from","file","location",".","Parameters","--","--","--","--","--","serial",":","Name","of","the","zip","file",".","Returns","--","--","--","-","deserialized","self","resulting","in","output","at","`","path","`",",","of","`","cls.save","(","path",")","`"]
code: def load_from_path(cls, serial):
      """Load object from file location.
  
      Parameters
      ----------
      serial : Name of the zip file.
  
      Returns
      -------
      deserialized self resulting in output at `path`, of `cls.save(path)`
      """
      import pickle
      from shutil import rmtree
      from zipfile import ZipFile
  
      from tensorflow import keras
  
      temp_unzip_loc = serial.parent / "temp_unzip/"
      temp_unzip_loc.mkdir()
  
      with ZipFile(serial, mode="r") as zip_file:
          for file in zip_file.namelist():
              if not file.startswith("keras/"):
                  continue
              zip_file.extract(file, temp_unzip_loc)
  
      keras_location = temp_unzip_loc / "keras"
      if keras_location.exists():
          cls.model_ = keras.models.load_model(keras_location)
      else:
          cls.model_ = None
  
      rmtree(temp_unzip_loc)
      cls.history = keras.callbacks.History()
      with ZipFile(serial, mode="r") as file:
          cls.history.set_params(
              pickle.loads(file.open("history").read())
          )
          return pickle.loads(file.open("_obj").read())
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from sklearn.utils import check_random_state
  from sktime.base._base import SERIALIZATION_FORMATS
  from sktime.classification.base import BaseClassifier
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 12 for calling the BaseDeepClassifier obj's underlying member method code with example usage: obj.load_from_path(cls, serial) and returns: pickle