function executeOp$h(node, tensorMap, context, resourceManager) {
      const value = ((node, tensorMap, context) => {
          switch (node.category) {
              case 'arithmetic':
                  return tfc.tidy(() => executeOp(node, tensorMap, context));
              case 'basic_math':
                  return tfc.tidy(() => executeOp$1(node, tensorMap, context));
              case 'control':
                  return executeOp$2(node, tensorMap, context);
              case 'convolution':
                  return tfc.tidy(() => executeOp$3(node, tensorMap, context));
              case 'creation':
                  return tfc.tidy(() => executeOp$4(node, tensorMap, context));
              case 'dynamic':
                  return executeOp$5(node, tensorMap, context);
              case 'evaluation':
                  return tfc.tidy(() => executeOp$6(node, tensorMap, context));
              case 'image':
                  return tfc.tidy(() => executeOp$9(node, tensorMap, context));
              case 'graph':
                  return tfc.tidy(() => executeOp$7(node, tensorMap, context));
              case 'logical':
                  return tfc.tidy(() => executeOp$a(node, tensorMap, context));
              case 'matrices':
                  return tfc.tidy(() => executeOp$b(node, tensorMap, context));
              case 'normalization':
                  return tfc.tidy(() => executeOp$c(node, tensorMap, context));
              case 'reduction':
                  return tfc.tidy(() => executeOp$d(node, tensorMap, context));
              case 'slice_join':
                  return tfc.tidy(() => executeOp$e(node, tensorMap, context));
              case 'spectral':
                  return tfc.tidy(() => executeOp$f(node, tensorMap, context));
              case 'transformation':
                  return tfc.tidy(() => executeOp$g(node, tensorMap, context));
              case 'hash_table':
                  return executeOp$8(node, tensorMap, context, resourceManager);
              case 'custom':
                  const opMapper = getRegisteredOp(node.op);
                  if (opMapper && opMapper.customExecutor) {
                      return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));
                  }
                  else {
                      throw TypeError(`Custom op ${node.op} is not registered.`);
                  }
              default:
                  throw TypeError(`Unknown op '${node.op}'. File an issue at ` +
                      `https://github.com/tensorflow/tfjs/issues so we can add it` +
                      `, or register a custom execution with tf.registerOp()`);
          }
      })(node, tensorMap, context);
      if (tfc.util.isPromise(value)) {
          return value.then((data) => [].concat(data));
      }
      return [].concat(value);
  }
/**
   * Executes the op defined by the node object.
   * @param node
   * @param tensorMap contains tensors for executed nodes and weights
   * @param context contains tensors and information for running the current node.
   * @param resourceManager Optional. Contains global resources of the model.
   */
Executes the op defined by the node object.
@param node
@param tensorMap contains tensors for executed nodes and weights
@param context contains tensors and information for running the current node.
@param resourceManager Optional. Contains global resources of the model.
Executes the op defined by the node object . @param node @param tensorMap contains tensors for executed nodes and weights @param context contains tensors and information for running the current node . @param resourceManager Optional . Contains global resources of the model .
function executeOp$h(node, tensorMap, context, resourceManager) {
      const value = ((node, tensorMap, context) => {
          switch (node.category) {
              case 'arithmetic':
                  return tfc.tidy(() => executeOp(node, tensorMap, context));
              case 'basic_math':
                  return tfc.tidy(() => executeOp$1(node, tensorMap, context));
              case 'control':
                  return executeOp$2(node, tensorMap, context);
              case 'convolution':
                  return tfc.tidy(() => executeOp$3(node, tensorMap, context));
              case 'creation':
                  return tfc.tidy(() => executeOp$4(node, tensorMap, context));
              case 'dynamic':
                  return executeOp$5(node, tensorMap, context);
              case 'evaluation':
                  return tfc.tidy(() => executeOp$6(node, tensorMap, context));
              case 'image':
                  return tfc.tidy(() => executeOp$9(node, tensorMap, context));
              case 'graph':
                  return tfc.tidy(() => executeOp$7(node, tensorMap, context));
              case 'logical':
                  return tfc.tidy(() => executeOp$a(node, tensorMap, context));
              case 'matrices':
                  return tfc.tidy(() => executeOp$b(node, tensorMap, context));
              case 'normalization':
                  return tfc.tidy(() => executeOp$c(node, tensorMap, context));
              case 'reduction':
                  return tfc.tidy(() => executeOp$d(node, tensorMap, context));
              case 'slice_join':
                  return tfc.tidy(() => executeOp$e(node, tensorMap, context));
              case 'spectral':
                  return tfc.tidy(() => executeOp$f(node, tensorMap, context));
              case 'transformation':
                  return tfc.tidy(() => executeOp$g(node, tensorMap, context));
              case 'hash_table':
                  return executeOp$8(node, tensorMap, context, resourceManager);
              case 'custom':
                  const opMapper = getRegisteredOp(node.op);
                  if (opMapper && opMapper.customExecutor) {
                      return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));
                  }
                  else {
                      throw TypeError(`Custom op ${node.op} is not registered.`);
                  }
              default:
                  throw TypeError(`Unknown op '${node.op}'. File an issue at ` +
                      `https://github.com/tensorflow/tfjs/issues so we can add it` +
                      `, or register a custom execution with tf.registerOp()`);
          }
      })(node, tensorMap, context);
      if (tfc.util.isPromise(value)) {
          return value.then((data) => [].concat(data));
      }
      return [].concat(value);
  }
function executeOp$h ( node , tensorMap , context , resourceManager ) { const value = ( ( node , tensorMap , context ) => { switch ( node . category ) { case 'arithmetic' : return tfc . tidy ( ( ) => executeOp ( node , tensorMap , context ) ) ; case 'basic_math' : return tfc . tidy ( ( ) => executeOp$1 ( node , tensorMap , context ) ) ; case 'control' : return executeOp$2 ( node , tensorMap , context ) ; case 'convolution' : return tfc . tidy ( ( ) => executeOp$3 ( node , tensorMap , context ) ) ; case 'creation' : return tfc . tidy ( ( ) => executeOp$4 ( node , tensorMap , context ) ) ; case 'dynamic' : return executeOp$5 ( node , tensorMap , context ) ; case 'evaluation' : return tfc . tidy ( ( ) => executeOp$6 ( node , tensorMap , context ) ) ; case 'image' : return tfc . tidy ( ( ) => executeOp$9 ( node , tensorMap , context ) ) ; case 'graph' : return tfc . tidy ( ( ) => executeOp$7 ( node , tensorMap , context ) ) ; case 'logical' : return tfc . tidy ( ( ) => executeOp$a ( node , tensorMap , context ) ) ; case 'matrices' : return tfc . tidy ( ( ) => executeOp$b ( node , tensorMap , context ) ) ; case 'normalization' : return tfc . tidy ( ( ) => executeOp$c ( node , tensorMap , context ) ) ; case 'reduction' : return tfc . tidy ( ( ) => executeOp$d ( node , tensorMap , context ) ) ; case 'slice_join' : return tfc . tidy ( ( ) => executeOp$e ( node , tensorMap , context ) ) ; case 'spectral' : return tfc . tidy ( ( ) => executeOp$f ( node , tensorMap , context ) ) ; case 'transformation' : return tfc . tidy ( ( ) => executeOp$g ( node , tensorMap , context ) ) ; case 'hash_table' : return executeOp$8 ( node , tensorMap , context , resourceManager ) ; case 'custom' : const opMapper = getRegisteredOp ( node . op ) ; if ( opMapper && opMapper . customExecutor ) { return opMapper . customExecutor ( new NodeValueImpl ( node , tensorMap , context ) ) ; } else { throw TypeError ( ` ${ node . op } ` ) ; } default : throw TypeError ( ` ${ node . op } ` + ` ` + ` ` ) ; } } ) ( node , tensorMap , context ) ; if ( tfc . util . isPromise ( value ) ) { return value . then ( ( data ) => [ ] . concat ( data ) ) ; } return [ ] . concat ( value ) ; }
Executes the op defined by the node object.
Executes the op defined by the node object .