code: def test_early_stopping_with_start_from_epoch(self):
      x_train = np.random.random((10, 5))
      y_train = np.random.random((10, 1))
      model = models.Sequential(
          (
              layers.Dense(1, activation="relu"),
              layers.Dense(1, activation="relu"),
          )
      )
      model.compile(optimizer="sgd", loss="mae", metrics=["mse"])
      start_from_epoch = 2
      patience = 3
      stopper = callbacks.EarlyStopping(
          monitor="mse",
          patience=patience,
          start_from_epoch=start_from_epoch,
      )
      history = model.fit(
          x_train, y_train, callbacks=[stopper], verbose=0, epochs=20
      )
      # Test 'patience' argument functions correctly when used
      # in conjunction with 'start_from_epoch'.
      self.assertGreaterEqual(
          len(history.epoch), patience + start_from_epoch
      )
  
      start_from_epoch = 2
      patience = 0
      stopper = callbacks.EarlyStopping(
          monitor="mse",
          patience=patience,
          start_from_epoch=start_from_epoch,
      )
      history = model.fit(
          x_train, y_train, callbacks=[stopper], verbose=0, epochs=20
      )
      # Test for boundary condition when 'patience' = 0.
      self.assertGreaterEqual(len(history.epoch), start_from_epoch)
  
file_path: keras/keras/callbacks/early_stopping_test.py
imports: import numpy
  import pytest
  from keras import callbacks
  from keras import layers
  from keras import metrics
  from keras import models
  from keras import ops
  from keras import testing
label_desc: Use image node_id 6 for calling the EarlyStoppingTest obj's underlying member method code with example usage: obj.test_early_stopping_with_start_from_epoch() without return types