- DifferentialEquation.perform(node, inputs_storage, output_storage)#
Calculate the function on the inputs and put the variables in the output storage.
The symbolic Apply node that represents this computation.
Immutable sequence of non-symbolic/numeric inputs. These are the values of each Variable in
List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each Variable in
node.outputs. The primary purpose of this method is to set the values of these sub-lists.
A tuple containing the values of each entry in
The output_storage list might contain data. If an element of output_storage is not
None, it has to be of the right type, for instance, for a TensorVariable, it has to be a NumPy
ndarraywith the right number of dimensions and the correct dtype. Its shape and stride pattern can be arbitrary. It is not guaranteed that such pre-set values were produced by a previous call to this
Op.perform(); they could’ve been allocated by another Op’s perform method. An Op is free to reuse output_storage as it sees fit, or to discard it and allocate new memory.