chore: import upstream snapshot with attribution

examples/flex-flows/eval-code-quality/README.md

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+# Eval Code Quality
+A example flow defined using class based entry which leverages model config to evaluate the quality of code snippet.
+
+## Prerequisites
+
+Install promptflow sdk and other dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Run flow
+
+- Prepare your Azure OpenAI resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one.
+
+- Setup connection
+
+Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.
+
+Or use CLI to create connection:
+
+```bash
+# Override keys with --set to avoid yaml file changes
+pf connection create --file ../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
+```
+
+Note in [flow.flex.yaml](flow.flex.yaml) we are using connection named `open_ai_connection`.
+```bash
+# show registered connection
+pf connection show --name open_ai_connection
+```
+
+- Run as normal Python file
+```bash
+python code_quality.py
+```
+
+- Test flow
+```bash
+# correct
+pf flow test --flow . --inputs code='print(\"Hello, world!\")' --init init.json
+
+# incorrect
+pf flow test --flow . --inputs code='printf("Hello, world!")' --init init.json
+```
+
+- Create run with multiple lines data
+
+```bash
+pf run create --flow . --init init.json --data ./data.jsonl --stream
+```
+
+Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI.
+
+- List and show run meta
+
+```bash
+# list created run
+pf run list
+
+# get a sample run name
+
+name=$(pf run list -r 10 | jq '.[] | select(.name | contains("eval_code_quality_")) | .name'| head -n 1 | tr -d '"')
+# show specific run detail
+pf run show --name $name
+
+# show output
+pf run show-details --name $name
+
+# show metrics
+pf run show-metrics --name $name
+
+# visualize run in browser
+pf run visualize --name $name
+```
+
+## Run flow in cloud
+
+- Assume we already have a connection named `open_ai_connection` in workspace.
+
+```bash
+# set default workspace
+az account set -s <your_subscription_id>
+az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name>
+```
+
+- Create run
+
+```bash
+# run with environment variable reference connection in azureml workspace
+pfazure run create --flow . --init init.json --data ./data.jsonl --stream