type ErasureInfo struct {
	// Algorithm is the string representation of erasure-coding-algorithm
	Algorithm string `json:"algorithm"`
	// DataBlocks is the number of data blocks for erasure-coding
	DataBlocks int `json:"data"`
	// ParityBlocks is the number of parity blocks for erasure-coding
	ParityBlocks int `json:"parity"`
	// BlockSize is the size of one erasure-coded block
	BlockSize int64 `json:"blockSize"`

     * is given by {@link #getSource()}.
     *
     * @return The one-based index of the line containing the problem or a non-positive value if unknown.
     */
    int getLineNumber();

    /**
     * Gets the one-based index of the column containing the problem. The column number should refer to some text file

                        "price": {
                            "title": "Price",
                            "type": "number",
                        },
                        "tax": {
                            "title": "Tax",
                            "anyOf": [{"type": "number"}, {"type": "null"}],
                        },
                        "tags": tags_schema,
                    },

//
//	 e1 := MD5(part-1)
//	 e2 := MD5(part-2)
//	...
//	 eN := MD5(part-N)
//
// Then, the ETag of the object is computed as MD5 of all individual
// part checksums. S3 also encodes the number of parts into the ETag
// by appending a -<number-of-parts> at the end:
//
//	ETag := MD5(e1 || e2 || e3 ... || eN) || -N
//
//	For example: ceb8853ddc5086cc4ab9e149f8f09c88-5
//

with 4 drives we get a total of 256 possible sets. So algorithm automatically chooses 64 sets, which is *16* 64 = 1024* drives in total.

- *If total number of nodes are of odd number then GCD algorithm provides affinity towards odd number erasure sets to provide for uniform distribution across nodes*. This is to ensure that same number of drives are pariticipating in any erasure set. For example if you have 2 nodes with 180 drives then GCD is 15 but this would lead to uneven distribution,...

}

// PendingTasks returns the number of ILM transition tasks waiting for a worker
// goroutine.
func (t *transitionState) PendingTasks() int {
	return len(t.transitionCh)
}

// ActiveTasks returns the number of active (ongoing) ILM transition tasks.
func (t *transitionState) ActiveTasks() int64 {
	return t.activeTasks.Load()
}

// MissedImmediateTasks returns the number of tasks - deferred to scanner due

                        },
                        "price": {"title": "Price", "type": "number"},
                        "tax": {
                            "title": "Tax",
                            "anyOf": [{"type": "number"}, {"type": "null"}],
                        },
                    },
                },
                "User": {

	} else {
		for i := 0; i < len(unMarshalXLMeta.Parts); i++ {
			if unMarshalXLMeta.Parts[i].Number != jsoniterXLMeta.Parts[i].Number {
				t.Errorf("Expected the number of part %d to be \"%d\", got \"%d\".", i+1, unMarshalXLMeta.Parts[i].Number, jsoniterXLMeta.Parts[i].Number)
			}
			if unMarshalXLMeta.Parts[i].Size != jsoniterXLMeta.Parts[i].Size {

	if err != nil {
		b.Fatal(err)
	}

	// get text data generated for number of bytes equal to object size.
	textData := generateBytesData(objSize)
	// generate md5sum for the generated data.
	// md5sum of the data to written is required as input for PutObject.

	md5hex := getMD5Hash(textData)
	sha256hex := ""

	// benchmark utility which helps obtain number of allocations and bytes allocated per ops.
	b.ReportAllocs()

## Validierung von Zahlen: Größer und kleiner oder gleich { #number-validations-greater-than-and-less-than-or-equal }

Das Gleiche gilt für:

* `gt`: `g`reater `t`han (größer als)
* `le`: `l`ess than or `e`qual (kleiner oder gleich)

{* ../../docs_src/path_params_numeric_validations/tutorial005_an_py39.py hl[10] *}

## Validierung von Zahlen: Floats, größer und kleiner { #number-validations-floats-greater-than-and-less-than }