nil, nil),
		prometheus.CounterValue,
		float64(connStats.s3InputBytes),
	)
}

// Populates prometheus with bucket usage metrics, this metrics
// is only enabled if scanner is enabled.
func bucketUsageMetricsPrometheus(ch chan<- prometheus.Metric) {
	objLayer := newObjectLayerFn()
	// Service not initialized yet
	if objLayer == nil {
		return
	}

pkg archive/zip, method (*Reader) RegisterDecompressor(uint16, Decompressor)
pkg archive/zip, method (*Writer) RegisterCompressor(uint16, Compressor)
pkg bufio, method (*Scanner) Buffer([]uint8, int)
pkg bufio, var ErrFinalToken error
pkg crypto/tls, const TLS_RSA_WITH_AES_128_GCM_SHA256 = 156
pkg crypto/tls, const TLS_RSA_WITH_AES_128_GCM_SHA256 uint16

You can use **type annotations** the same way you would for input data in function **parameters**, you can use Pydantic models, lists, dictionaries, scalar values like integers, booleans, etc.

//// tab | Python 3.10+

```Python hl_lines="16  21"
{!> ../../docs_src/response_model/tutorial001_01_py310.py!}
```

////

//// tab | Python 3.9+

		// case guessEmbeddedHas:
		default:
			schema.err = fmt.Errorf("invalid field found for struct %v's field %s: define a valid foreign key for relations or implement the Valuer/Scanner interface",
				schema, field.Name)
		}
	}

	switch gl {
	case guessBelongs:
		primarySchema, foreignSchema = relation.FieldSchema, schema
	case guessEmbeddedBelongs:
		if field.OwnerSchema == nil {

      found_shape = true;
    }
  }
  return found_dtype && found_shape;
}

bool IsScalarConst(const tensorflow::NodeDef& node_def, int v) {
  if (node_def.op() != "Const" || node_def.name() != "scalar") {
    return false;
  }
  bool found_dtype = false;
  bool found_value = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {

        Keras training loops like `fit`/`evaluate`, the unreduced vector loss is
        passed to the optimizer but the reported loss will be a scalar value.
    *   `SUM`: Scalar sum of weighted losses. 4. `SUM_OVER_BATCH_SIZE`: Scalar
        `SUM` divided by number of elements in losses. This reduction type is
        not supported when used with `tf.distribute.Strategy` outside of

pkg text/scanner, method (*Position) IsValid() bool
pkg text/scanner, method (*Scanner) Init(io.Reader) *Scanner
pkg text/scanner, method (*Scanner) IsValid() bool
pkg text/scanner, method (*Scanner) Next() int32
pkg text/scanner, method (*Scanner) Peek() int32
pkg text/scanner, method (*Scanner) Pos() Position
pkg text/scanner, method (*Scanner) Scan() int32
pkg text/scanner, method (*Scanner) TokenText() string

    if (dst->dims() != 0) {
      return InvalidArgument(
          "Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with "
          "shape ",
          dst->shape().DebugString());
    }
    *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape());
    if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString(
            string(static_cast<const char*>(tensor_interface->Data()),

| `minio_node_process_uptime_seconds`        | Uptime for MinIO process per node in seconds.                                                                   |

## Scanner Metrics

| Name                                       | Description                                                 |
|:-------------------------------------------|:------------------------------------------------------------|

// into `dims`. `dims` must be an array large enough to hold `num_dims`
// entries (e.g., the return value of TF_GraphGetTensorNumDims).
//
// If the number of dimensions in the shape is unknown or the shape is
// a scalar, `dims` will remain untouched. Otherwise, each element of
// `dims` will be set corresponding to the size of the dimension. An
// unknown dimension is represented by `-1`.
//
// Returns an error into `status` if: