def unIndentBlock(block):
            currentIndent = blockIndentation(block)

            if currentIndent.endswith('\t'):
                charsToRemove = 1
            elif withSpace:
                charsToRemove = 1 if currentIndent else 0
            else:
                if self.useTabs:
                    charsToRemove = min(spacesCount(currentIndent), self.width)
                else:  # spaces
                    if currentIndent.endswith(self.text()):  # remove indent level
                        charsToRemove = self.width
                    else:  # remove all spaces
                        charsToRemove = min(spacesCount(currentIndent), self.width)

            if charsToRemove:
                cursor = cursorAtSpaceEnd(block)
                cursor.setPosition(cursor.position() - charsToRemove, QTextCursor.KeepAnchor)
                cursor.removeSelectedText()
def nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)