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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def broadcast_to(self, shape):
 """Broadcasts the input array to a new shape. Broadcasting is only allowed on axes with size 1. The new shape cannot change the number of dimensions. For example, you could broadcast from shape (2, 1) to (2, 3), but not from shape (2, 3) to (2, 3, 3). Parameters shape : tuple of int The shape of the desired array. Returns ------- NDArray A NDArray with the desired shape that is not sharing data with this array, even if the new shape is the same as ``self.shape``. Examples -------- array([[[ 0.], [ 1.], [ 2.]]], dtype=float32) array([[[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]], <BLANKLINE> [[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]]], dtype=float32) """

        cur_shape = self.shape
        err_str = 'operands could not be broadcast together with remapped shapes' \
                  '[original->remapped]: {} and requested shape {}'.format(cur_shape, shape)
        if len(shape) < len(cur_shape):
            raise ValueError(err_str)
        cur_shape = (1,) * (len(shape) - len(cur_shape)) + cur_shape
        cur_shape_arr = np.array(cur_shape)
        broadcasting_axes = np.nonzero(cur_shape_arr != np.array(shape))
        if (cur_shape_arr[broadcasting_axes] != 1).any():
            raise ValueError(err_str)
        if cur_shape != self.shape:
            return op.broadcast_to(self.reshape(cur_shape), shape=shape)
        else:
            return op.broadcast_to(self, shape=tuple(shape))