import tensorflow as tf

    bias_var_shape = [nf] if one_dim_bias else [1, nf, 1, 1]
    nin = x.get_shape()[channel_ax].value
    wshape = [rf, rf, nin, nf]
    with tf.variable_scope(scope):
        w = tf.get_variable("w", wshape, initializer=ortho_init(init_scale))
        b = tf.get_variable("b", bias_var_shape, initializer=tf.constant_initializer(0.0))
        if not one_dim_bias and data_format == 'NHWC':
            b = tf.reshape(b, bshape)
        return tf.nn.conv2d(x, w, strides=strides, padding=pad, data_format=data_format) + b

def fc(x, scope, nh, *, init_scale=1.0, init_bias=0.0):
    with tf.variable_scope(scope):
        nin = x.get_shape()[1].value
        w = tf.get_variable("w", [nin, nh], initializer=ortho_init(init_scale))
        print("w is "+str(w))
        b = tf.get_variable("b", [nh], initializer=tf.constant_initializer(init_bias))
        return tf.matmul(x, w)+b

def batch_to_seq(h, nbatch, nsteps, flat=False):
    if flat:
        h = tf.reshape(h, [nbatch, nsteps])
    else:
        h = tf.reshape(h, [nbatch, nsteps, -1])
    return [tf.squeeze(v, [1]) for v in tf.split(axis=1, num_or_size_splits=nsteps, value=h)]

def seq_to_batch(h, flat = False):
    shape = h[0].get_shape().as_list()
    if not flat:
        assert(len(shape) > 1)
        nh = h[0].get_shape()[-1].value
