ttrs","SAFE_URL_PATTERN","DATA_URL_PATTERN","sanitizeHtml","unsafeHtml","whiteList","sanitizeFn","createdDocument","DOMParser","parseFromString","whitelistKeys","keys","el","elName","nodeName","attributeList","attributes","whitelistedAttributes","concat","allowedAttributeList","attrName","nodeValue","regExp","attrRegex","allowedAttribute","innerHTML","BSCLS_PREFIX_REGEX","DISALLOWED_ATTRIBUTES","HOVER_STATE_SHOW","HOVER_STATE_OUT","TRIGGER_HOVER","TRIGGER_FOCUS","AttachmentMap","AUTO","TOP","RIG...

    FileSystem fs =
        Jimfs.newFileSystem(
            Configuration.unix().toBuilder()
                .setSupportedFeatures(ObjectArrays.concat(SYMBOLIC_LINKS, supportedFeatures))
                .build());
    Files.createDirectories(fs.getPath("dir/b/i/j/l"));
    Files.createFile(fs.getPath("dir/a"));
    Files.createFile(fs.getPath("dir/c"));

      ImmutableList.of(-0.0, 0.0, 1.0, 100.0, 10000.0, Double.MAX_VALUE);

  private static final Iterable<Double> TOLERANCE_CANDIDATES =
      ImmutableList.copyOf(
          Iterables.concat(
              FINITE_TOLERANCE_CANDIDATES, ImmutableList.of(Double.POSITIVE_INFINITY)));

  private static final ImmutableList<Double> BAD_TOLERANCE_CANDIDATES =
      ImmutableList.copyOf(
          Doubles.asList(

values and operands to divide\n  // them by ofset X and Y.\n  const splitRegex = /\\s*,\\s*|\\s+/;\n  let ops = divider !== -1\n    ? [\n        fragments\n          .slice(0, divider)\n          .concat([fragments[divider].split(splitRegex)[0]]),\n        [fragments[divider].split(splitRegex)[1]].concat(\n          fragments.slice(divider + 1)\n        ),\n      ]\n    : [fragments];\n\n  // Convert the values with units to absolute pixels to allow our computations\n  ops = ops.map((op, index) =>...

arseItem","search","searchValue","val","toLowerCase","close","searchResults","filter","name","includes","endResults","slice","result","_renderItem","encodeURI","path","open","itemObject","navLink","navTreeview","end","_trimText","push","newPath","concat","trim","_this4","join","decodeURI","regExp","RegExp","str","groupItemElement","decodeURIComponent","searchTitleElement","searchPathElement","keyCode","last","focus","$focused","prev","resetOnClose","NavbarSearch","POSITION_TOP_RIGHT","POSITION_T...

{"version":3,"sources":["jquery-3.7.1.js"],"names":["global","factory","module","exports","document","w","Error","window","this","noGlobal","arr","getProto","Object","getPrototypeOf","slice","flat","array","call","concat","apply","push","indexOf","class2type","toString","hasOwn","hasOwnProperty","fnToString","ObjectFunctionString","support","isFunction","obj","nodeType","item","isWindow","preservedScriptAttributes","type","src","nonce","noModule","DOMEval","code","node","doc","i","val","scrip...

{"version":3,"sources":["jquery-3.7.1.js"],"names":["global","factory","module","exports","document","w","Error","window","this","noGlobal","arr","getProto","Object","getPrototypeOf","slice","flat","array","call","concat","apply","push","indexOf","class2type","toString","hasOwn","hasOwnProperty","fnToString","ObjectFunctionString","support","isFunction","obj","nodeType","item","isWindow","preservedScriptAttributes","type","src","nonce","noModule","DOMEval","code","node","doc","i","val","scrip...

i={},o=n.default(e).clone().find("> .nav-link"),l=n.default(e).clone().find("> .nav-treeview"),s=o.attr("href"),r=o.find("p").children().remove().end().text();if(i.name=this._trimText(r),i.link=s,i.path=t,0===l.length)ct.push(i);else{var d=i.path.concat([i.name]);l.children().each((function(e,t){a._parseItem(t,d)}))}}},a._trimText=function(e){return t.trim(e.replace(/(\r\n|\n|\r)/gm," "))},a._renderItem=function(e,t,a){var i=this;if(a=a.join(" "+this.options.arrowSign+" "),e=unescape(e),t=decode...

Tag","einem Tag"],dd:[e+" Tage",e+" Tagen"],w:["eine Woche","einer Woche"],M:["ein Monat","einem Monat"],MM:[e+" Monate",e+" Monaten"],y:["ein Jahr","einem Jahr"],yy:[e+" Jahre",e+" Jahren"]};return a?n[t][0]:n[t][1]}function Fs(e,a,t,s){var n={m:["eine Minute","einer Minute"],h:["eine Stunde","einer Stunde"],d:["ein Tag","einem Tag"],dd:[e+" Tage",e+" Tagen"],w:["eine Woche","einer Woche"],M:["ein Monat","einem Monat"],MM:[e+" Monate",e+" Monaten"],y:["ein Jahr","einem Jahr"],yy:[e+" Jahre",e+" Jahren"]};return...

match NumPy order as `tf.sparse_split(sp_input, num_split, axis)`. NOTE: we have
temporarily made `tf.sparse_split` require keyword arguments. * `tf.concat` now
takes arguments in reversed order and with different keywords. In particular we
now match NumPy order as `tf.concat(values, axis, name)`. *
`tf.image.decode_jpeg` by default uses the faster DCT method, sacrificing a