--from-file=keycloak.crt ``` If TLS is not enabled, you would need only the third party CA: ``` kubectl -n minio create secret generic minio-trusted-certs --from-file=keycloak.crt ``` The name of the generated secret can then be passed to Helm using a values file or the `--set` parameter: ``` trustedCertsSecret: "minio-trusted-certs" or --set trustedCertsSecret=minio-trusted-certs ``` Create buckets after install ---------- Install the chart, specifying the buckets you want to create after install: ```bash helm install...

--generate-name minio/minio ``` The command deploys MinIO on the Kubernetes cluster in the default configuration. The [configuration](#configuration) section lists the parameters that can be configured during installation. Upgrading the Chart ---------- You can use Helm to update MinIO version in a live release. Assuming your release is named as `my-release`, get the values using the command: ```bash helm get values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml`...

--generate-name minio/minio ``` The command deploys MinIO on the Kubernetes cluster in the default configuration. The [configuration](#configuration) section lists the parameters that can be configured during installation. Upgrading the Chart ---------- You can use Helm to update MinIO version in a live release. Assuming your release is named as `my-release`, get the values using the command: ```bash helm get values my-release > old_values.yaml ``` Then change the field `image.tag` in `old_values.yaml`...

   * values (i.e. it is the square root of the mean of the squares of the vertical distances between
   * the data points and the best fit line). For this fit, this error is a fraction {@code sqrt(1 -
   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *

/// tip

Note that the test function is now `async def` instead of just `def` as before when using the `TestClient`.

///

Then we can create an `AsyncClient` with the app, and send async requests to it, using `await`.

{* ../../docs_src/async_tests/app_a_py39/test_main.py hl[9:12] *}

This is the equivalent to:

```Python
response = client.get('/')
```

            path: CAs/public.crt
```

Note that the `secretName` should be same as the secret name created in previous step. Then add the below section under
`spec.containers[].volumeMounts[]`

```yaml
    volumeMounts:
        - name: secret-volume
          mountPath: /<user-running-minio>/.minio/certs
```

	}
	return n, err
}

// readFrom populates the content of the current file by reading from r.
// The bytes read must match the number of remaining bytes in the current file.
//
// If the current file is sparse and r is an io.ReadSeeker,
// then readFrom uses Seek to skip past holes defined in Header.SparseHoles,
// assuming that skipped regions are all NULs.
// This always reads the last byte to ensure r is the right size.
//

And then we validate that data with the Pydantic model (catching the `ValidationError` exception), and if we get an error reading the JWT token or validating the data with Pydantic, we raise the `HTTPException` we created before.

For that, we update the Pydantic model `TokenData` with a new property `scopes`.

# `Request` class

You can declare a parameter in a *path operation function* or dependency to be of type `Request` and then you can access the raw request object directly, without any validation, etc.

You can import it directly from `fastapi`:

```python
from fastapi import Request
```

/// tip

#!/bin/bash
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
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# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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