Generalized convolution layer.

def general_conv(x,
                 num_filters=64,
                 filter_size=7,
                 stride=1,
                 stddev=0.02,
                 padding="VALID",
                 name="conv",
                 do_norm="instance",
                 do_relu=True,
                 relufactor=0):
  """Generalized convolution layer."""
  with tf.variable_scope(name):
    x = layers().Conv2D(
        num_filters,
        filter_size,
        stride,
        padding,
        activation=None,
        kernel_initializer=tf.truncated_normal_initializer(stddev=stddev),
        bias_initializer=tf.constant_initializer(0.0))(x)
    if do_norm == "layer":
      x = layer_norm(x)
    elif do_norm == "instance":
      x = instance_norm(x)

    if do_relu:
      if relufactor == 0:
        x = tf.nn.relu(x, "relu")
      else:
        x = lrelu(x, leak=relufactor)

    return x