ppai/image-classification: add exponential retries to download file (#6668)

## Description

Fixes #6463

Even though the `Connection reset by peer` is a transient error and we can't do much to solve it on our side, we retry the request with exponential backoff and hopefully it'll work at some point. This will hopefully decrease the number of flaky test runs.

## Checklist
- [ ] I have followed [Sample Guidelines from AUTHORING_GUIDE.MD](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md)
- [ ] README is updated to include [all relevant information](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md#readme-file)
- [ ] **Tests** pass:   `nox -s py-3.6` (see [Test Environment Setup](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md#test-environment-setup))
- [ ] **Lint** pass:   `nox -s lint` (see [Test Environment Setup](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/AUTHORING_GUIDE.md#test-environment-setup))
- [ ] These samples need a new **API enabled** in testing projects to pass (let us know which ones)
- [ ] These samples need a new/updated **env vars** in testing projects set to pass (let us know which ones)
- [ ] Please **merge** this PR for me once it is approved.
- [ ] This sample adds a new sample directory, and I updated the [CODEOWNERS file](https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/.github/CODEOWNERS) with the codeowners for this sample

Author: davidcavazos

people-and-planet-ai/image-classification/predict.py

@@ -21,6 +21,8 @@
 from google.cloud.aiplatform.gapic.schema import predict
 import requests
 
+from train_model import with_retries
+
 
 def run(
     project: str, region: str, model_endpoint_id: str, image_file: str
@@ -43,7 +45,7 @@ def run(
     )
 
     base_url = "https://lilablobssc.blob.core.windows.net/wcs-unzipped"
-    image_bytes = requests.get(f"{base_url}/{image_file}").content
+    image_bytes = with_retries(lambda: requests.get(f"{base_url}/{image_file}").content)
 
     response = client.predict(
         endpoint=client.endpoint_path(