message: Fix save/load test.
  Reviewers: #tensorflow, #framework_ip_review_-_any_oss_or_third-party_code_use_has_been_approved, georgep
  Maniphest Tasks: T23738
diff: @@ -71,7 +71,7 @@ class IPUPipelineTest(test.TestCase):
  strategy = ipu.ipu_strategy.IPUStrategy()
  with strategy.scope():
  - m = ipu.keras.PipelinedModel(fixed_weight_pipeline(),
  + m = ipu.keras.SequentialPipelineModel(fixed_weight_pipeline(),
  gradient_accumulation_count=24)
  opt = keras.optimizer_v2.gradient_descent.SGD(learning_rate=0.001)
  m.compile(opt, loss='mse')
  @@ -98,7 +98,7 @@ class IPUPipelineTest(test.TestCase):
  # Restore the weights and check that they are back to the trained
  with strategy.scope():
  - m = ipu.keras.PipelinedModel(fixed_weight_pipeline(),
  + m = ipu.keras.SequentialPipelineModel(fixed_weight_pipeline(),
  gradient_accumulation_count=24)
  opt = keras.optimizer_v2.gradient_descent.SGD(learning_rate=0.001)
  m.compile(opt, loss='mse')
  @@ -136,7 +136,7 @@ class IPUPipelineTest(test.TestCase):
  strategy = ipu.ipu_strategy.IPUStrategy()
  with strategy.scope():
  - m = ipu.keras.PipelinedModel(fixed_weight_pipeline(),
  + m = ipu.keras.SequentialPipelineModel(fixed_weight_pipeline(),
  gradient_accumulation_count=24)
  opt = keras.optimizer_v2.gradient_descent.SGD(learning_rate=0.001)
  m.compile(opt, loss='mse')
  @@ -154,7 +154,7 @@ class IPUPipelineTest(test.TestCase):
  with strategy.scope():
  # Create a new model and train (should restore from checkpoint)
  - m = ipu.keras.PipelinedModel(fixed_weight_pipeline(),
  + m = ipu.keras.SequentialPipelineModel(fixed_weight_pipeline(),
  gradient_accumulation_count=24)
  opt = keras.optimizer_v2.gradient_descent.SGD(learning_rate=0.001)
  m.compile(opt, loss='mse')
  @@ -176,7 +176,7 @@ class IPUPipelineTest(test.TestCase):
  strategy = ipu.ipu_strategy.IPUStrategy()
  with strategy.scope():
  - m = ipu.keras.PipelinedModel(fixed_weight_pipeline(),
  + m = ipu.keras.SequentialPipelineModel(fixed_weight_pipeline(),
  gradient_accumulation_count=24)
  opt = keras.optimizer_v2.gradient_descent.SGD(learning_rate=0.001)
  m.compile(opt, loss='mse')
  