/// TensorFlow checks that the plugin supplies all mandatory operations and
/// then copies these tables to a different memory location, marking the new
/// operation tables as read-only. Once a plugin is loaded, none of these
/// operation pointers may change.
///
/// There are 4 function tables: one for each of the 3 file objects in
/// TensorFlow (i.e., `RandomAccessFile`, `WritableFile`,

// DefaultPhysPageSize is the default physical page size.
const DefaultPhysPageSize = _DefaultPhysPageSize

// PCQuantum is the minimal unit for a program counter (1 on x86, 4 on most other systems).
// The various PC tables record PC deltas pre-divided by PCQuantum.
const PCQuantum = _PCQuantum

// Int64Align is the required alignment for a 64-bit integer (4 on 32-bit systems, 8 on 64-bit).
const Int64Align = PtrSize

An ORM has tools to convert ("*map*") between *objects* in code and database tables ("*relations*").

With an ORM, you normally create a class that represents a table in a SQL database, each attribute of the class represents a column, with a name and a type.

For example a class `Pet` could represent a SQL table `pets`.

And each *instance* object of that class represents a row in the database.

//
// The simple (and slow) CRC32 implementation only uses a 256*4 bytes table.
//
// The slicing-by-8 algorithm is a faster implementation that uses a bigger
// table (8*256*4 bytes).

package crc32

import "internal/byteorder"

// simpleMakeTable allocates and constructs a Table for the specified
// polynomial. The table is suitable for use with the simple algorithm
// (simpleUpdate).

  - pymdownx.inlinehilite
  - pymdownx.magiclink
  - pymdownx.smartsymbols
  - pymdownx.superfences
  - pymdownx.tilde
  - pymdownx.tabbed:
      alternate_style: true
  - tables

plugins:
  - search
  - redirects:
      redirect_maps:
        # Redirect all feature pages to features/*
        'caching.md': 'features/caching.md'
        'calls.md': 'features/calls.md'

    }
  }

  /**
   * Returns the bucket (in either the K-to-V or V-to-K tables) where elements with the specified
   * hash could be found, if present, or could be inserted.
   */
  private int bucket(int hash) {
    return hash & (hashTableKToV.length - 1);
  }

  /** Given a key, returns the index of the entry in the tables, or ABSENT if not found. */
  int findEntryByKey(@CheckForNull Object key) {

	ScanType() reflect.Type
	Comment() (value string, ok bool)
	DefaultValue() (value string, ok bool)
}

type Index interface {
	Table() string
	Name() string
	Columns() []string
	PrimaryKey() (isPrimaryKey bool, ok bool)
	Unique() (unique bool, ok bool)
	Option() string
}

// TableType table type interface
type TableType interface {
	Schema() string
	Name() string
	Type() string
	Comment() (comment string, ok bool)

// A p256Table holds the first 15 multiples of a point at offset -1, so [1]P
// is at table[0], [15]P is at table[14], and [0]P is implicitly the identity
// point.
type p256Table [15]*P256Point

// Select selects the n-th multiple of the table base point into p. It works in
// constant time by iterating over every entry of the table. n must be in [0, 15].
func (table *p256Table) Select(p *P256Point, n uint8) {
	if n >= 16 {

// native TFLite variables or not.
std::unique_ptr<OperationPass<ModuleOp>> CreateAnalyzeVariablesPass();

// Creates a pass which is responsible for legalizing TensorFlow static hash
// tables to TensorFlow Lite hash tables.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeHashTablesPass();

// Creates get arithmetic count pass, which will calculate the arithmetic count
// for each ops.

	}

	// Parameter "obj" is a table from server; print it.
	// display tables following the rules of options
	if table, ok := obj.(*metav1.Table); ok {
		// Do not print headers if this table has no column definitions, or they are the same as the last ones we printed
		localOptions := h.options
		if h.printedHeaders && (len(table.ColumnDefinitions) == 0 || reflect.DeepEqual(table.ColumnDefinitions, h.lastColumns)) {