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)

/**
 * 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

  if (num_dims > TensorShape::MaxDimensions()) {
    return absl::InvalidArgumentError(
        absl::StrCat("Value specified for `", attr_name, "` has ", num_dims,
                     " dimensions which is over the limit of ",
                     TensorShape::MaxDimensions(), "."));
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {

        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.

                    /*input_shapes*/ {make_shape({3, 2}), unknown_shape()},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, kUnknownDim}));

  // Infer shape when some dimensions are unknown.
  CheckOutputShapes(
      matmul_op,
      /*input_shapes*/ {make_shape({kUnknownDim, 2}), make_shape({2, 4})},
      /*input_tensors*/ {},
      /*expected_shape*/ make_shape({kUnknownDim, 4}));

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.

  // Fetch the shape and validate it is 2 by -1.
  num_dims = TF_GraphGetTensorNumDims(graph, feed_out_0, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(2, num_dims);

  // Resize the dimension vector appropriately.
  int64_t returned_dims[2];
  TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  EXPECT_EQ(dims[0], returned_dims[0]);

The elements can be addressed by integer <a href="#Index_expressions">indices</a>
0 through <code>len(a)-1</code>.
Array types are always one-dimensional but may be composed to form
multi-dimensional types.
</p>

<pre>
[32]byte
[2*N] struct { x, y int32 }
[1000]*float64
[3][5]int
[2][2][2]float64  // same as [2]([2]([2]float64))
</pre>

<p>

file. package mldsa import ( "bytes" "crypto/internal/fips140" "crypto/internal/fips140/drbg" "crypto/internal/fips140/sha3" "crypto/internal/fips140/subtle" "crypto/internal/fips140deps/byteorder" "errors" ) type parameters struct { k, l int // dimensions of A η int // bound for secret coefficients γ1 int // log₂(γ₁), where [-γ₁+1, γ₁] is the bound of y γ2 int // denominator of γ₂ = (q - 1) / γ2 λ int // collison strength τ int // number of non-zero coefficients in challenge ω int // max number of...