code: def create_pipeline(
      pipeline_name: str,
      pipeline_root: str,
      data_path: str,
      preprocessing_fn: str,
      run_fn: str,
      train_args: tfx.proto.TrainArgs,
      eval_args: tfx.proto.EvalArgs,
      eval_accuracy_threshold: float,
      serving_model_dir: str,
      schema_path: Optional[str] = None,
      metadata_connection_config: Optional[
          metadata_store_pb2.ConnectionConfig
      ] = None,
      beam_pipeline_args: Optional[List[str]] = None,
  ) -> tfx.dsl.Pipeline:
      """Implements the penguin pipeline with TFX."""
  
      components = []
  
      # Brings data into the pipeline or otherwise joins/converts training data.
      # TODO(step 2): Might use another ExampleGen class for your data.
      example_gen = tfx.components.CsvExampleGen(input_base=data_path)
      components.append(example_gen)
  
      # Computes statistics over data for visualization and example validation.
      statistics_gen = tfx.components.StatisticsGen(
          examples=example_gen.outputs["examples"]
      )
      components.append(statistics_gen)
  
      if schema_path is None:
          # Generates schema based on statistics files.
          schema_gen = tfx.components.SchemaGen(
              statistics=statistics_gen.outputs["statistics"]
          )
          components.append(schema_gen)
      else:
          # Import user provided schema into the pipeline.
          schema_gen = tfx.components.ImportSchemaGen(
              schema_file=schema_path
          )
          components.append(schema_gen)
  
          # Performs anomaly detection based on statistics and data schema.
          example_validator = tfx.components.ExampleValidator(  # pylint: disable=unused-variable
              statistics=statistics_gen.outputs["statistics"],
              schema=schema_gen.outputs["schema"],
          )
          components.append(example_validator)
  
      # Performs transformations and feature engineering in training and serving.
      transform = (
          tfx.components.Transform(  # pylint: disable=unused-variable
              examples=example_gen.outputs["examples"],
              schema=schema_gen.outputs["schema"],
              preprocessing_fn=preprocessing_fn,
          )
      )
      # TODO(step 3): Uncomment here to add Transform to the pipeline.
      # components.append(transform)
  
      # Uses user-provided Python function that implements a model using Tensorflow.
      trainer = tfx.components.Trainer(
          run_fn=run_fn,
          examples=example_gen.outputs["examples"],
          # Use outputs of Transform as training inputs if Transform is used.
          # examples=transform.outputs['transformed_examples'],
          # transform_graph=transform.outputs['transform_graph'],
          schema=schema_gen.outputs["schema"],
          train_args=train_args,
          eval_args=eval_args,
      )
      # TODO(step 4): Uncomment here to add Trainer to the pipeline.
      # components.append(trainer)
  
      # Get the latest blessed model for model validation.
      model_resolver = tfx.dsl.Resolver(
          strategy_class=tfx.dsl.experimental.LatestBlessedModelStrategy,
          model=tfx.dsl.Channel(
              type=tfx.types.standard_artifacts.Model
          ),
          model_blessing=tfx.dsl.Channel(
              type=tfx.types.standard_artifacts.ModelBlessing
          ),
      ).with_id("latest_blessed_model_resolver")
      # TODO(step 5): Uncomment here to add Resolver to the pipeline.
      # components.append(model_resolver)
  
      # Uses TFMA to compute a evaluation statistics over features of a model and
      # perform quality validation of a candidate model (compared to a baseline).
      eval_config = tfma.EvalConfig(
          model_specs=[
              tfma.ModelSpec(
                  signature_name="serving_default",
                  label_key=features.LABEL_KEY,
                  # Use transformed label key if Transform is used.
                  # label_key=features.transformed_name(features.LABEL_KEY),
                  preprocessing_function_names=["transform_features"],
              )
          ],
          slicing_specs=[tfma.SlicingSpec()],
          metrics_specs=[
              tfma.MetricsSpec(
                  metrics=[
                      tfma.MetricConfig(
                          class_name="SparseCategoricalAccuracy",
                          threshold=tfma.MetricThreshold(
                              value_threshold=tfma.GenericValueThreshold(
                                  lower_bound={
                                      "value": eval_accuracy_threshold
                                  }
                              ),
                              change_threshold=tfma.GenericChangeThreshold(
                                  direction=tfma.MetricDirection.HIGHER_IS_BETTER,
                                  absolute={"value": -1e-10},
                              ),
                          ),
                      )
                  ]
              )
          ],
      )
      evaluator = tfx.components.Evaluator(  # pylint: disable=unused-variable
          examples=example_gen.outputs["examples"],
          model=trainer.outputs["model"],
          baseline_model=model_resolver.outputs["model"],
          # Change threshold will be ignored if there is no baseline (first run).
          eval_config=eval_config,
      )
      # TODO(step 5): Uncomment here to add Evaluator to the pipeline.
      # components.append(evaluator)
  
      # Pushes the model to a file destination if check passed.
      pusher = tfx.components.Pusher(  # pylint: disable=unused-variable
          model=trainer.outputs["model"],
          model_blessing=evaluator.outputs["blessing"],
          push_destination=tfx.proto.PushDestination(
              filesystem=tfx.proto.PushDestination.Filesystem(
                  base_directory=serving_model_dir
              )
          ),
      )
      # TODO(step 5): Uncomment here to add Pusher to the pipeline.
      # components.append(pusher)
  
      return tfx.dsl.Pipeline(
          pipeline_name=pipeline_name,
          pipeline_root=pipeline_root,
          components=components,
          # Change this value to control caching of execution results. Default value
          # is `False`.
          # enable_cache=True,
          metadata_connection_config=metadata_connection_config,
          beam_pipeline_args=beam_pipeline_args,
      )
  
imports: from typing import List, Optional
  import tensorflow_model_analysis
  from tfx import v1
  from tfx.experimental.templates.penguin.models import features
  from ml_metadata.proto import metadata_store_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: create_pipeline(pipeline_name, pipeline_root, data_path, preprocessing_fn, run_fn, train_args, eval_args, eval_accuracy_threshold, serving_model_dir, schema_path, metadata_connection_config, beam_pipeline_args) and returns: tfx