func isIAMPolicyReplicated(cntReplicated, total int, policies []*policy.Policy) bool {
	if cntReplicated > 0 && cntReplicated != total {
		return false
	}
	// check if policies match between sites
	var prev *policy.Policy
	for i, p := range policies {
		if i == 0 {
			prev = p
			continue
		}
		if !prev.Equals(*p) {
			return false
		}
	}
	return true
}

	var testTime = time.Date(2009, time.November, 10, 23, 45, 58, 0, time.UTC)
	fh := new(FileHeader)
	fh.SetModTime(testTime)
	outTime := fh.ModTime()
	if !outTime.Equal(testTime) {
		t.Errorf("times don't match: got %s, want %s", outTime, testTime)
	}
}

func testHeaderRoundTrip(fh *FileHeader, wantUncompressedSize uint32, wantUncompressedSize64 uint64, t *testing.T) {
	fi := fh.FileInfo()
	fh2, err := FileInfoHeader(fi)

        """
        Add a *path operation* using an HTTP PATCH operation.

        ## Example

        ```python
        from fastapi import FastAPI
        from pydantic import BaseModel

        class Item(BaseModel):
            name: str
            description: str | None = None

        app = FastAPI()

        @app.patch("/items/")
        def update_item(item: Item):

That, plus the simple fact that Python is the main language for **Data Science**, Machine Learning and especially Deep Learning, make FastAPI a very good match for Data Science / Machine Learning web APIs and applications (among many others).

To see how to achieve this parallelism in production see the section about [Deployment](deployment/index.md){.internal-link target=_blank}.

			}
			r.Close()
			if !bytes.Equal(testCase.data, buffer.Bytes()) {
				t.Errorf("Test %d: %s: Data Mismatch: Data fetched back from the uploaded object doesn't match the original one.", i+1, instanceType)
			}
		}
	}
}

// Wrapper for calling PutObject API handler tests for both Erasure multiple disks and FS single drive setup.

		if globalDNSConfig != nil {
			// Background this operation.
			bootstrapTrace("go initFederatorBackend", func() {
				go initFederatorBackend(buckets, newObject)
			})
		}

		// Initialize batch job pool.
		bootstrapTrace("newBatchJobPool", func() {
			globalBatchJobPool = newBatchJobPool(GlobalContext, newObject, 100)
			globalBatchJobsMetrics = batchJobMetrics{
				metrics: make(map[string]*batchJobInfo),

	    and result values, corresponding parameter and result types are
	    identical, and either both functions are variadic or neither is.
	    Parameter and result names are not required to match.</li>

	<li>Two interface types are identical if they define the same type set.
	</li>

	<li>Two map types are identical if they have identical key and element types.</li>

	if wantID == "" {
		return nil
	}
	id, err := p.storage.GetDiskID()
	if err != nil {
		return err
	}
	if id != wantID {
		return fmt.Errorf("disk ID %s does not match. disk reports %s", wantID, id)
	}
	return nil
}

// diskHealthCheckOK will check if the provided error is nil
// and update disk status if good.

github.com/tidwall/gjson v1.18.0 h1:FIDeeyB800efLX89e5a8Y0BNH+LOngJyGrIWxG2FKQY=
github.com/tidwall/gjson v1.18.0/go.mod h1:/wbyibRr2FHMks5tjHJ5F8dMZh3AcwJEMf5vlfC0lxk=
github.com/tidwall/match v1.1.1 h1:+Ho715JplO36QYgwN9PGYNhgZvoUSc9X2c80KVTi+GA=
github.com/tidwall/match v1.1.1/go.mod h1:eRSPERbgtNPcGhD8UCthc6PmLEQXEWd3PRB5JTxsfmM=
github.com/tidwall/pretty v1.2.0/go.mod h1:ITEVvHYasfjBbM0u2Pg8T2nJnzm8xPwvNhhsoaGGjNU=

channel.basic_consume(callback,
                      queue=queue_name,
                      no_ack=False)

channel.start_consuming()
```

Execute this example python program to watch for RabbitMQ events on the console.

```py
python rabbit.py
```

Open another terminal and upload a JPEG image into `images` bucket.

```
mc cp myphoto.jpg myminio/images
```