// 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:
//   * `output` is not in `graph`.

		This    string `json:"this"`    // This field carries assigned disk uuid.
		// Sets field carries the input disk order generated the first
		// time when fresh disks were supplied, it is a two dimensional
		// array second dimension represents list of disks used per set.
		Sets [][]string `json:"sets"`
		// Distribution algorithm represents the hashing algorithm
		// to pick the right set index for an object.

labels.search_result_sort_filename_desc=Nome file (desc)
labels.search_result_sort_created_asc=Data (asc)
labels.search_result_sort_created_desc=Data (desc)
labels.search_result_sort_content_length_asc=Dimensione (asc)
labels.search_result_sort_content_length_desc=Dimensione (desc)
labels.search_result_sort_last_modified_asc=Ultima modifica (asc)
labels.search_result_sort_last_modified_desc=Ultima modifica (desc)
labels.search_result_sort_click_count_asc=Clic (asc)

// are cached and refreshed after a given timeout.
//
// For metrics with a `bucket` dimension, a list of buckets argument is required
// to collect the metrics.
//
// It implements the prometheus.Collector interface for metric groups without a
// bucket dimension. For metric groups with a bucket dimension, use the
// `GetBucketCollector` method to get a `BucketCollector` that implements the

/**
 * HTML tag-based thumbnail generator that creates thumbnails from image content
 * referenced in HTML documents. This generator extracts images from HTML content
 * and processes them to create thumbnail images based on configured dimensions
 * and format settings.
 *
 * <p>The generator validates image MIME types, processes image data through
 * ImageIO operations, and applies scaling and cropping to generate thumbnails

        control flow.
    *   Add `RaggedTensor.numpy()`.
    *   Update `RaggedTensor.__getitem__` to preserve uniform dimensions & allow
        indexing into uniform dimensions.
    *   Update `tf.expand_dims` to always insert the new dimension as a
        non-ragged dimension.
    *   Update `tf.embedding_lookup` to use `partition_strategy` and `max_norm`
        when `ids` is ragged.

constraints.Null.message = {item} deve essere nullo.
constraints.Past.message = {item} deve essere una data passata.
constraints.Pattern.message = {item} non corrisponde a "{regexp}".
constraints.Size.message = La dimensione di {item} deve essere compresa tra {min} e {max} caratteri.
# ----------------------------------------------------------
# Hibernate Validator
# -------------------

import java.util.Map;
import java.util.Objects;
import java.util.Set;
import java.util.Spliterator;
import org.jspecify.annotations.Nullable;

/**
 * Fixed-size {@link Table} implementation backed by a two-dimensional array.
 *
 * <p><b>Warning:</b> {@code ArrayTable} is rarely the {@link Table} implementation you want. First,
 * it requires that the complete universe of rows and columns be specified at construction time.

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this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown())...