- MeasurableElemwise.perform(node, inputs, output_storage)#
Calculate the function on the inputs and put the variables in the output storage.
node – The symbolic
Applynode that represents this computation.
inputs – Immutable sequence of non-symbolic/numeric inputs. These are the values of each
output_storage – List of mutable single-element lists (do not change the length of these lists). Each sub-list corresponds to value of each
node.outputs. The primary purpose of this method is to set the values of these sub-lists.
params – A tuple containing the values of each entry in
output_storagelist 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
Opis free to reuse
output_storageas it sees fit, or to discard it and allocate new memory.