import tensorflow as tf

   return out


def intprod(x):
   return int(np.prod(x))


def numel(x):
   return intprod(var_shape(x))


def flatgrad(loss, var_list, clip_norm=None):
    grads = tf.gradients(loss, var_list)
    if clip_norm is not None:
        grads = [tf.clip_by_norm(grad, clip_norm=clip_norm) for grad in grads]
    return tf.concat(axis=0, values=[
        tf.reshape(grad if grad is not None else tf.zeros_like(v), [numel(v)])
        for (v, grad) in zip(var_list, grads)
    ])


class SetFromFlat(object):
    def __init__(self, var_list, dtype=tf.float32):
        assigns = []
        shapes = list(map(var_shape, var_list))
        total_size = np.sum([intprod(shape) for shape in shapes])

        self.theta = theta = tf.placeholder(dtype, [total_size])
