code: def _model_fn(features, labels, mode, config):
      del labels
      del mode
      del config
      global_step = training_util.get_or_create_global_step()
      update_global_step_op = global_step.assign_add(1)
      latest_feature = variable_v1.VariableV1(
          0, name="latest_feature", dtype=dtypes.int64
      )
      store_latest_feature_op = latest_feature.assign(features)
      ops.add_to_collection("my_vars", global_step)
      ops.add_to_collection("my_vars", latest_feature)
      return estimator_lib.EstimatorSpec(
          mode="train",
          train_op=control_flow_ops.group(
              [update_global_step_op, store_latest_feature_op]
          ),
          loss=constant_op.constant(2.0),
      )
  
imports: from absl.testing import parameterized
  from tensorflow.python.checkpoint import checkpoint_management
  from tensorflow.python.data.experimental.ops import iterator_ops
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.estimator import estimator_lib
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import variable_v1
  from tensorflow.python.platform import test
  from tensorflow.python.training import saver
  from tensorflow.python.training import training_util
label_desc: Use image node_id 1 for calling the CheckpointInputPipelineHookTest obj's underlying member method code with example usage: obj._model_fn(features, labels, mode, config) and returns: estimator_lib