code: def pipeline_with_one_container_spec_component() -> tfx.dsl.Pipeline:
      """Pipeline with container."""
  
      importer_task = tfx.dsl.Importer(
          source_uri="some-uri",
          artifact_type=tfx.types.standard_artifacts.Model,
      ).with_id("my_importer")
  
      container_task = DummyContainerSpecComponent(
          input1=importer_task.outputs["result"],
          output1=channel_utils.as_channel(
              [tfx.types.standard_artifacts.Model()]
          ),
          param1="value1",
      )
  
      return tfx.dsl.Pipeline(
          pipeline_name="pipeline-with-container",
          pipeline_root=_TEST_PIPELINE_ROOT,
          components=[importer_task, container_task],
      )
  
imports: import datetime
  import os
  from typing import List
  from kfp.pipeline_spec import pipeline_spec_pb2
  import tensorflow_model_analysis
  from tfx import v1
  from tfx.components.example_gen import utils
  from tfx.components.trainer.executor import Executor
  from tfx.dsl.component.experimental import executor_specs
  from tfx.dsl.component.experimental import placeholders
  from tfx.dsl.components.base import base_component
  from tfx.dsl.components.base import base_executor
  from tfx.dsl.components.base import base_node
  from tfx.dsl.components.base import executor_spec
  from tfx.types import channel_utils
  from tfx.types import component_spec
  from tfx.types.experimental import simple_artifacts
  from tfx.utils import proto_utils
  from google.protobuf import message
label_desc: Use image node_id 8 for calling a global function with example usage: pipeline_with_one_container_spec_component() and returns: tfx