def apply(self, *variables_list):
ctx = Autograd.Context()
input_tensors = [v.data for v in variables_list]
forward_tensor = self.forward(ctx, *input_tensors)
output_variable = Variable(forward_tensor)
output_variable.backward_function = lambda x: self.backward(ctx, x)
output_variable.backward_variables = [v for v in variables_list]
return output_variable
def apply(cls, *variables_list):
new_context = Context()
input_tensors = [v.data for v in variables_list]
forward_tensor = cls.forward(new_context, *input_tensors)
output_variable = Variable(forward_tensor)
output_variable.backward_function = lambda x: cls.backward(
new_context, x)
output_variable.backward_variables = [v for v in variables_list]
return output_variable