from fastapi.testclient import TestClient

from docs_src.metadata.tutorial001_1_py39 import app

client = TestClient(app)


def test_items():
    response = client.get("/items/")
    assert response.status_code == 200, response.text
    assert response.json() == [{"name": "Katana"}]


def test_openapi_schema():
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text

":"IAAfALXQ7neb6tB6cUCF+4KWy+9fUZvEWix5dwG72xq/MaLqipdU1TkEKPj6gM1I==","X-Minio-Internal-Server-Side-Encryption-Seal-Algorithm":"DAREv2-HMAC-SHA256","content-type":"text/plain"}},{"Bucket":"buck1","Name":"go_113/src/cmd/go/testdata/norunexample/test_test.go","UserDef":{"X-Minio-Internal-Server-Side-Encryption-Iv":"L5vvKTfMywAcpu6YhWo5=","X-Minio-Internal-Server-Side-Encryption-S3-Kms-Key-Id":"my-minio-key","X-Minio-Internal-Server-Side-Encryption-S3-Kms-Sealed-Key":"IAAfAIc4dh6XQAe19TCE+QWstk+CR...

        `(batch_size, 1)`. This enables `Model` subclasses to process scalar
        data in their `train_step()`/`test_step()`/`predict_step()` methods. \
        Note that this change may break certain subclassed models. You can
        revert back to the previous behavior by adding upranking yourself in the
        `train_step()`/`test_step()`/`predict_step()` methods, e.g. `if
        x.shape.rank == 1: x = tf.expand_dims(x, axis=-1)`. Functional models as