This change allows directly passing the PipelineConf instance to compiler or launcher which makes it easier to create portable pipelines by allowing the environment-specific configuration to be directly passed to the environment-specific launcher.

Background:
PipelineConf holds all pipeline-level configuration including `op_transformers`, `image_pull_secrets` etc. Some of these are specific to particular execution environment (e.g. GCP secret or Argo artifact location or Kubernetes-specific options).
Previously, the only way to modify `PipelineConf` was to do it inside the piepline function. That tied the pipeline function to specific execution environment (e.g. GCP, Argo or Kubernetes)

Solution: This change allows directly passing the PipelineConf instance to compiler or launcher. This allows writing portable enlauncher and environment agnostic pipeline functions. All environment-specific configurations can be moved to launching stage.

Before:
```python
# Defining pipeline
def my_pipeline():
    # portable pipeline code

    dsl.get_pipeline_conf().add_op_transformer(gcp.use_gcp_secret('user-gcp-sa'))

# Launching pipeline
kfp.Clinet().create_run_from_pipeline_func(my_pipeline, arguments={})
```

After:
```python
# Defining pipeline
def my_pipeline():
    # portable pipeline code

# Launching pipeline
pipeline_conf = dsl.PipelineConf()
pipeline_conf.add_op_transformer(gcp.use_gcp_secret('user-gcp-sa'))
kfp.Clinet().create_run_from_pipeline_func(my_pipeline, arguments={}, pipeline_conf=pipeline_conf)
```

After 2 *(launching same portable pipeline using different launchers):
```python
# Loading portable pipeline
from portable_pipeline import my_pipeline

# Launching pipeline on Kubeflow
pipeline_conf = dsl.PipelineConf()
pipeline_conf.add_op_transformer(gcp.use_gcp_secret('user-gcp-sa'))
kfp.Clinet().create_run_from_pipeline_func(my_pipeline, arguments={}, pipeline_conf=pipeline_conf)

# Launching pipeline on locally (not implemented yet)
kfp.run_pipeline_func_locally(my_pipeline, arguments={})
```