class_docstr: An old model that should always reload from its equally old checkpoints.
  
  Don't change this model architecture or add any fancy features or the test won't be valid.
class_docstr_tok: ["An","old","model","that","should","always","reload","from","its","equally","old","checkpoints",".","Do","n't","change","this","model","architecture","or","add","any","fancy","features","or","the","test","wo","n't","be","valid","."]
code: def __init__(self, context: keras.TFKerasTrialContext):
      self.context = context
      self.my_batch_size = self.context.get_per_slot_batch_size()
      self.my_learning_rate = self.context.get_hparam("learning_rate")
  
file_path: determined/harness/tests/experiment/fixtures/ancient_keras_ckpt.py
imports: from typing import Any, Dict, cast
  import tensorflow
  from tensorflow.keras.layers import Dense
  from tensorflow.keras.losses import mean_squared_error
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.optimizers.legacy import SGD
  from tensorflow.raw_ops import ZipDataset
  from determined import keras
label_desc: Use image node_id 1 to create a new AncientTrial object from inherited base classes: keras with example: obj = AncientTrial(context)