A computer-implemented method comprising: maintaining, by a computational graph system that manages execution of computational graphs representing neural network operations for users of the computational graph system, data specifying a plurality of pre-trained neural networks, wherein each of the pre-trained neural networks is a neural network that has been trained on training data to determine trained values of the respective parameters of the neural network; receiving from a user, via a user interface presented to a user, input data specifying a user computational graph representing neural network operations, the user computational graph comprising a plurality of nodes connected by edges; receiving from the user a user input specifying an insertion point after a first node in the user computational graph; identifying a particular computational graph for a particular pre-trained neural network from the plurality of pre-trained neural networks, the identifying comprising analyzing the pre-trained neural networks in the plurality of pre-trained neural networks to identify a neural network that takes inputs that have the same dimensions and size as an output tensor generated by the first node and that generates outputs that have the same dimensions and size that are required by a destination in the user computational graph; and inserting a remote call node into the user computational graph, wherein the remote call node is a node of the user computational graph which is configured to, during execution of the user computational graph: receive an output tensor generated by the first node of the user computational graph, provide, using a remote call, the output tensor as input to the particular computational graph for the particular trained neural network, obtain, in response to the remote call, a network output generated by executing the particular computational graph for the particular trained neural network, and provide the network output generated by the particular trained neural network as an output of the remote call node; receiving a graph input to the user computational graph; and executing the user computational graph with the inserted remote call node on the graph input. maintaining, by a computational graph system that manages execution of computational graphs representing neural network operations for users of the computational graph system, data specifying a plurality of pre-trained neural networks, wherein each of the pre-trained neural networks is a neural network that has been trained on training data to determine trained values of the respective parameters of the neural network; receiving from a user, via a user interface presented to a user, input data specifying a user computational graph representing neural network operations, the user computational graph comprising a plurality of nodes connected by edges; receiving from the user a user input specifying an insertion point after a first node in the user computational graph; identifying a particular computational graph for a particular pre-trained neural network from the plurality of pre-trained neural networks, the identifying comprising analyzing the pre-trained neural networks in the plurality of pre-trained neural networks to identify a neural network that takes inputs that have the same dimensions and size as an output tensor generated by the first node and that generates outputs that have the same dimensions and size that are required by a destination in the user computational graph; and inserting a remote call node into the user computational graph, wherein the remote call node is a node of the user computational graph which is configured to, during execution of the user computational graph: receive an output tensor generated by the first node of the user computational graph, provide, using a remote call, the output tensor as input to the particular computational graph for the particular trained neural network, obtain, in response to the remote call, a network output generated by executing the particular computational graph for the particular trained neural network, and provide the network output generated by the particular trained neural network as an output of the remote call node; receive an output tensor generated by the first node of the user computational graph, provide, using a remote call, the output tensor as input to the particular computational graph for the particular trained neural network, obtain, in response to the remote call, a network output generated by executing the particular computational graph for the particular trained neural network, and provide the network output generated by the particular trained neural network as an output of the remote call node; receiving a graph input to the user computational graph; and executing the user computational graph with the inserted remote call node on the graph input.