Using ParallelRunConfig and DockerConfig not working

Hello, I am attempting to use the ParallelRunStep for a set of python scripts that I currently run inside a docker container. I am trying to deploy this using an AzureML pipeline that reads from the docker image saved in our ACR. However, the init and run scripts in my parallel script never seem to be able to access the files I have moved into the docker container. Particularly when I try and search for a hydra config file in my init and run scripts, I am unable to find the file, even when I look for it explicitly. I have included a snippet that is based on the example code from this repo: [example of parallel run config](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-partition-per-column.ipynb)

Any idea what I am doing wrong? I have included a snippet of what I am trying below

```import os

docker_config = DockerConfiguration(use_docker=True, arguments=docker_args)

environment_name = "my-environment"
environment = Environment(environment_name)
base_image_name = os.getenv("ACR_BASE_IMAGE_NAME")
base_image_tag = os.getenv("ACR_IMAGE_TAG")
environment.docker.base_image = f"{base_image_name}:{base_image_tag}"

environment.docker.base_image_registry.address = f"{acr_name}.azurecr.io"

environment.docker.base_image_registry.username = os.getenv("ACR_USER")
environment.docker.base_image_registry.password = os.getenv("ACR_PASSWORD")
environment.python.user_managed_dependencies = True
environment.docker.enabled=True

run_config = RunConfiguration()
run_config.environment = environment
run_config.docker = docker_config

parallel_run_config = ParallelRunConfig(
source_directory=".",
entry_script="path/to/docker/script_1.py",
compute_target=compute_target,
environment=environment,
node_count=2,
error_threshold=10,
output_action="append_row",
mini_batch_size=1,
logging_level='DEBUG'
)
step_parallel = ParallelRunStep(
name="parallel-step",
parallel_run_config=parallel_run_config,
inputs=my_inputs,
output=output_dir,
arguments=args,
allow_reuse=True,
)

step_parallel._runconfig.docker = docker_config #Tried with and without, does not seem to make a difference

pipeline_steps = StepSequence(steps=[step_parallel])
pipeline_run = Pipeline(workspace=ws, steps=pipeline_steps)

# Submit your pipeline run
submitted_pipeline_run = Experiment(ws, "Azure Pipeline").submit(pipeline_run, regenerate_outputs=True)```

從連結的平行執行 notebook 開始，將其中的 ParallelRunConfig、DockerConfiguration 和 source_directory 設定與提供的程式碼片段進行比較。追蹤 path/to/docker/script_1.py 以及 init 和 run 指令碼，以判斷它們如何在 image 中定位檔案。當 pipeline 執行時，指令碼能可靠地找到預期的 Hydra 設定，即表示完成。

由索引模型根據 Issue 內容生成。