Dataflow Prime job fails when providing resource hints on a transform

I have a classic Dataflow template written using the Apache Beam Java SDK v2.32.0. The template simply consumes messages from a Pub/Sub subscription and writes them to Google Cloud Storage.

The template can successfully be used to run jobs with [Dataflow Prime](https://cloud.google.com/dataflow/docs/guides/enable-dataflow-prime) experimental features enabled through `\-\-additional-experiments enable_prime` and providing a pipeline level resource hint using `\-\-parameters=resourceHints=min_ram=8GiB`:
```

gcloud dataflow jobs run my-job-name \
--additional-experiments enable_prime \
--disable-public-ips
\
--gcs-location gs://bucket/path/to/template \
--num-workers 1 \
--max-workers 16 \
--parameters=resourceHints=min_ram=8GiB,other_pipeline_options=true
\
--project my-project \
--region us-central1 \
--service-account-email my-service-account@my-project.iam.gserviceaccount.com
\
--staging-location gs://bucket/path/to/staging
--subnetwork https://www.googleapis.com/compute/v1/projects/my-project/regions/us-central1/subnetworks/my-subnet

```

In an attempt to use Dataflow Prime's [Right Fitting](https://cloud.google.com/dataflow/docs/guides/right-fitting) capability, I change the pipeline code to include a resource hint on the FileIO transform:
```

class WriteGcsFileTransform
extends PTransform, WriteFilesResult>
{

private static final long serialVersionUID = 1L;

@Override
public WriteFilesResult
expand(PCollection input) {

return input.apply(
FileIO.writeDynamic()

.by(myDynamicDestinationFunction)
.withDestinationCoder(Destination.coder())

.withNumShards(8)
.withNaming(myDestinationFileNamingFunction)

.withTempDirectory("gs://bucket/path/to/temp")
.withCompression(Compression.GZIP)

.setResourceHints(ResourceHints.create().withMinRam("32GiB"))
);
}

```

Attempting to run jobs from a template based on the new code results in a continuous crash loop with the job never successfully running. The lone repeated error log entry is:
```

{
"insertId": "s=97e1ecd30e0243609d555685318325b4;i=4e1;b=6c7f5d65f3994eada5f20672dab1daf1;m=912f16c;t=5d024689cb030;x=b36751718b3d80c1",

"jsonPayload": {
"line": "pod_workers.go:191",
"message": "Error syncing pod 4cf7cbf98df4b5e2d054abce7da1262b
(\"df-df-hvm-my-job-name-11061310-qn51-harness-jb9f_default(4cf7c6bf982df4b5eb2d054abce7da12)\"), skipping:
failed to \"StartContainer\" for \"artifact\" with CrashLoopBackOff: \"back-off 40s restarting failed
container=artifact pod=df-df-hvm-my-job-name-11061310-qn51-harness-jb9f_default(4cf7c6bf982df4b5eb2d054abce7da12)\"",

"thread": "807"
},
"resource": {
"type": "dataflow_step",
"labels": {
"project_id":
"my-project",
"region": "us-central1",
"step_id": "",
"job_id": "2021-11-06_12_10_27-510057810808146686",

"job_name": "my-job-name"
}
},
"timestamp": "2021-11-06T20:14:36.052491Z",
"severity":
"ERROR",
"labels": {
"compute.googleapis.com/resource_type": "instance",
"dataflow.googleapis.com/log_type":
"system",
"compute.googleapis.com/resource_id": "4695846446965678007",
"dataflow.googleapis.com/job_name":
"my-job-name",
"dataflow.googleapis.com/job_id": "2021-11-06_12_10_27-510057810808146686",

"dataflow.googleapis.com/region": "us-central1",
"dataflow.googleapis.com/service_option": "prime",

"compute.googleapis.com/resource_name": "df-hvm-my-job-name-11061310-qn51-harness-jb9f"
},

"logName": "projects/my-project/logs/dataflow.googleapis.com%2Fkubelet",
"receiveTimestamp": "2021-11-06T20:14:46.471285909Z"
}

```

If the pipeline level resources hints and step level resources hint are both set to 8GiB, the pipeline fails with the same repetitive error.

Imported from Jira [BEAM-13225](https://issues.apache.org/jira/browse/BEAM-13225). Original Jira may contain additional context.
Reported by: brentworden.