arrow/format-0ed34c83.patch

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//
//   http://www.apache.org/licenses/LICENSE-2.0
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diff --git a/format/Message.fbs b/format/Message.fbs
index 1a7e0dfff..f1c18d765 100644
--- a/format/Message.fbs
+++ b/format/Message.fbs
@@ -28,7 +28,7 @@ namespace org.apache.arrow.flatbuf;
 /// Metadata about a field at some level of a nested type tree (but not
 /// its children).
 ///
-/// For example, a List<Int16> with values [[1, 2, 3], null, [4], [5, 6], null]
+/// For example, a List<Int16> with values `[[1, 2, 3], null, [4], [5, 6], null]`
 /// would have {length: 5, null_count: 2} for its List node, and {length: 6,
 /// null_count: 0} for its Int16 node, as separate FieldNode structs
 struct FieldNode {
diff --git a/format/Schema.fbs b/format/Schema.fbs
index 3b37e5d85..3b00dd478 100644
--- a/format/Schema.fbs
+++ b/format/Schema.fbs
@@ -110,10 +110,11 @@ table FixedSizeList {
 /// not enforced.
 ///
 /// Map
+/// ```text
 ///   - child[0] entries: Struct
 ///     - child[0] key: K
 ///     - child[1] value: V
-///
+/// ```
 /// Neither the "entries" field nor the "key" field may be nullable.
 ///
 /// The metadata is structured so that Arrow systems without special handling
@@ -129,7 +130,7 @@ enum UnionMode:short { Sparse, Dense }
 /// A union is a complex type with children in Field
 /// By default ids in the type vector refer to the offsets in the children
 /// optionally typeIds provides an indirection between the child offset and the type id
-/// for each child typeIds[offset] is the id used in the type vector
+/// for each child `typeIds[offset]` is the id used in the type vector
 table Union {
   mode: UnionMode;
   typeIds: [ int ]; // optional, describes typeid of each child.
diff --git a/format/SparseTensor.fbs b/format/SparseTensor.fbs
index 3fe8a7582..a6fd2f9e7 100644
--- a/format/SparseTensor.fbs
+++ b/format/SparseTensor.fbs
@@ -37,21 +37,21 @@ namespace org.apache.arrow.flatbuf;
 ///
 /// For example, let X be a 2x3x4x5 tensor, and it has the following
 /// 6 non-zero values:
-///
+/// ```text
 ///   X[0, 1, 2, 0] := 1
 ///   X[1, 1, 2, 3] := 2
 ///   X[0, 2, 1, 0] := 3
 ///   X[0, 1, 3, 0] := 4
 ///   X[0, 1, 2, 1] := 5
 ///   X[1, 2, 0, 4] := 6
-///
+/// ```
 /// In COO format, the index matrix of X is the following 4x6 matrix:
-///
+/// ```text
 ///   [[0, 0, 0, 0, 1, 1],
 ///    [1, 1, 1, 2, 1, 2],
 ///    [2, 2, 3, 1, 2, 0],
 ///    [0, 1, 0, 0, 3, 4]]
-///
+/// ```
 /// When isCanonical is true, the indices is sorted in lexicographical order
 /// (row-major order), and it does not have duplicated entries.  Otherwise,
 /// the indices may not be sorted, or may have duplicated entries.
@@ -86,26 +86,27 @@ table SparseMatrixIndexCSX {
 
   /// indptrBuffer stores the location and size of indptr array that
   /// represents the range of the rows.
-  /// The i-th row spans from indptr[i] to indptr[i+1] in the data.
+  /// The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data.
   /// The length of this array is 1 + (the number of rows), and the type
   /// of index value is long.
   ///
   /// For example, let X be the following 6x4 matrix:
-  ///
+  /// ```text
   ///   X := [[0, 1, 2, 0],
   ///         [0, 0, 3, 0],
   ///         [0, 4, 0, 5],
   ///         [0, 0, 0, 0],
   ///         [6, 0, 7, 8],
   ///         [0, 9, 0, 0]].
-  ///
+  /// ```
   /// The array of non-zero values in X is:
-  ///
+  /// ```text
   ///   values(X) = [1, 2, 3, 4, 5, 6, 7, 8, 9].
-  ///
+  /// ```
   /// And the indptr of X is:
-  ///
+  /// ```text
   ///   indptr(X) = [0, 2, 3, 5, 5, 8, 10].
+  /// ```
   indptrBuffer: Buffer (required);
 
   /// The type of values in indicesBuffer
@@ -116,9 +117,9 @@ table SparseMatrixIndexCSX {
   /// The type of index value is long.
   ///
   /// For example, the indices of the above X is:
-  ///
+  /// ```text
   ///   indices(X) = [1, 2, 2, 1, 3, 0, 2, 3, 1].
-  ///
+  /// ```
   /// Note that the indices are sorted in lexicographical order for each row.
   indicesBuffer: Buffer (required);
 }
@@ -126,7 +127,7 @@ table SparseMatrixIndexCSX {
 /// Compressed Sparse Fiber (CSF) sparse tensor index.
 table SparseTensorIndexCSF {
   /// CSF is a generalization of compressed sparse row (CSR) index.
-  /// See [smith2017knl]: http://shaden.io/pub-files/smith2017knl.pdf
+  /// See [smith2017knl](http://shaden.io/pub-files/smith2017knl.pdf)
   ///
   /// CSF index recursively compresses each dimension of a tensor into a set
   /// of prefix trees. Each path from a root to leaf forms one tensor
@@ -135,7 +136,7 @@ table SparseTensorIndexCSF {
   ///
   /// For example, let X be a 2x3x4x5 tensor and let it have the following
   /// 8 non-zero values:
-  ///
+  /// ```text
   ///   X[0, 0, 0, 1] := 1
   ///   X[0, 0, 0, 2] := 2
   ///   X[0, 1, 0, 0] := 3
@@ -144,9 +145,9 @@ table SparseTensorIndexCSF {
   ///   X[1, 1, 1, 0] := 6
   ///   X[1, 1, 1, 1] := 7
   ///   X[1, 1, 1, 2] := 8
-  ///
+  /// ```
   /// As a prefix tree this would be represented as:
-  ///
+  /// ```text
   ///         0          1
   ///        / \         |
   ///       0   1        1
@@ -154,24 +155,24 @@ table SparseTensorIndexCSF {
   ///     0   0   1      1
   ///    /|  /|   |    /| |
   ///   1 2 0 2   0   0 1 2
-
+  /// ```
   /// The type of values in indptrBuffers
   indptrType: Int (required);
 
   /// indptrBuffers stores the sparsity structure.
   /// Each two consecutive dimensions in a tensor correspond to a buffer in
-  /// indptrBuffers. A pair of consecutive values at indptrBuffers[dim][i]
-  /// and indptrBuffers[dim][i + 1] signify a range of nodes in
-  /// indicesBuffers[dim + 1] who are children of indicesBuffers[dim][i] node.
+  /// indptrBuffers. A pair of consecutive values at `indptrBuffers[dim][i]`
+  /// and `indptrBuffers[dim][i + 1]` signify a range of nodes in
+  /// `indicesBuffers[dim + 1]` who are children of `indicesBuffers[dim][i]` node.
   ///
   /// For example, the indptrBuffers for the above X is:
-  ///
+  /// ```text
   ///   indptrBuffer(X) = [
   ///                       [0, 2, 3],
   ///                       [0, 1, 3, 4],
   ///                       [0, 2, 4, 5, 8]
   ///                     ].
-  ///
+  /// ```
   indptrBuffers: [Buffer] (required);
 
   /// The type of values in indicesBuffers
@@ -180,22 +181,22 @@ table SparseTensorIndexCSF {
   /// indicesBuffers stores values of nodes.
   /// Each tensor dimension corresponds to a buffer in indicesBuffers.
   /// For example, the indicesBuffers for the above X is:
-  ///
+  /// ```text
   ///   indicesBuffer(X) = [
   ///                        [0, 1],
   ///                        [0, 1, 1],
   ///                        [0, 0, 1, 1],
   ///                        [1, 2, 0, 2, 0, 0, 1, 2]
   ///                      ].
-  ///
+  /// ```
   indicesBuffers: [Buffer] (required);
 
   /// axisOrder stores the sequence in which dimensions were traversed to
   /// produce the prefix tree.
   /// For example, the axisOrder for the above X is:
-  ///
+  /// ```text
   ///   axisOrder(X) = [0, 1, 2, 3].
-  ///
+  /// ```
   axisOrder: [int] (required);
 }