itypes= [TensorType(int8, matrix), TensorType(int8, matrix), TensorType(float32, 3D), TensorType(int32, matrix)]¶
otypes= [TensorType(int32, matrix)]¶
perform(node, inputs_storage, output_storage)¶
Required: Calculate the function on the inputs and put the variables in the output storage. Return None.
- node : Apply instance
Contains the symbolic inputs and outputs.
- inputs : list
Sequence of inputs (immutable).
- output_storage : list
List of mutable 1-element lists (do not change the length of these lists)
The subclass does not override this method.
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 ndarray, with the right number of dimensions, and the correct dtype. Its shape and stride pattern, can be arbitrary. It not is guaranteed that it was produced by a previous call to impl. It could be allocated by another Op impl is free to reuse it as it sees fit, or to discard it and allocate new memory.