def make_node(self, *matrices):
if not matrices:
raise ValueError("no matrices to allocate")
matrices = list(map(at.as_tensor, matrices))
if any(mat.type.ndim != 2 for mat in matrices):
raise TypeError("all data arguments must be matrices")
if self.sparse:
out_type = aesara.sparse.matrix(
self.format, dtype=largest_common_dtype(matrices))
else:
out_type = aesara.tensor.matrix(
dtype=largest_common_dtype(matrices))
return Apply(self, matrices, [out_type])
def perform(self, node, inputs, output_storage, params=None):
dtype = largest_common_dtype(inputs)
if self.sparse:
output_storage[0][0] = sp.sparse.block_diag(
inputs, self.format, dtype)
else:
output_storage[0][0] = scipy_block_diag(*inputs).astype(dtype)