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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 conv(name, x, output_channels, filter_size=None, stride=None, logscale_factor=3.0, apply_actnorm=True, conv_init="default", dilations=None):
 """Convolutional layer with edge bias padding and optional actnorm. If x is 5-dimensional, actnorm is applied independently across every time-step. Args: name: variable scope. x: 4-D Tensor or 5-D Tensor of shape NHWC or NTHWC output_channels: Number of output channels. filter_size: list of ints, if None [3, 3] and [2, 3, 3] are defaults for 4-D and 5-D input tensors respectively. stride: list of ints, default stride: 1 logscale_factor: see actnorm for parameter meaning. apply_actnorm: if apply_actnorm the activations of the first minibatch have zero mean and unit variance. Else, there is no scaling applied. conv_init: default or zeros. default is a normal distribution with 0.05 std. dilations: List of integers, apply dilations. Returns: x: actnorm(conv2d(x)) Raises: ValueError: if init is set to "zeros" and apply_actnorm is set to True. """

  if conv_init == "zeros" and apply_actnorm:
    raise ValueError("apply_actnorm is unstable when init is set to zeros.")

  x_shape = common_layers.shape_list(x)
  is_2d = len(x_shape) == 4
  num_steps = x_shape[1]

  # set filter_size, stride and in_channels
  if is_2d:
    if filter_size is None:
      filter_size = [3, 3]
    if stride is None:
      stride = [1, 1]
    if dilations is None:
      dilations = [1, 1, 1, 1]
    actnorm_func = actnorm
    x = add_edge_bias(x, filter_size=filter_size)
    conv_filter = tf.nn.conv2d
  else:
    if filter_size is None:
      if num_steps == 1:
        filter_size = [1, 3, 3]
      else:
        filter_size = [2, 3, 3]
    if stride is None:
      stride = [1, 1, 1]
    if dilations is None:
      dilations = [1, 1, 1, 1, 1]
    actnorm_func = actnorm_3d
    x = time_pad(x, filter_size=filter_size, dilations=dilations)
    conv_filter = tf.nn.conv3d

  in_channels = common_layers.shape_list(x)[-1]
  filter_shape = filter_size + [in_channels, output_channels]
  stride_shape = [1] + stride + [1]

  with tf.variable_scope(name, reuse=tf.AUTO_REUSE):

    if conv_init == "default":
      initializer = default_initializer()
    elif conv_init == "zeros":
      initializer = tf.zeros_initializer()

    w = tf.get_variable("W", filter_shape, tf.float32, initializer=initializer)
    x = conv_filter(x, w, stride_shape, padding="VALID", dilations=dilations)
    if apply_actnorm:
      x, _ = actnorm_func("actnorm", x, logscale_factor=logscale_factor)
    else:
      x += tf.get_variable("b", [1, 1, 1, output_channels],
                           initializer=tf.zeros_initializer())
      logs = tf.get_variable("logs", [1, output_channels],
                             initializer=tf.zeros_initializer())
      x *= tf.exp(logs * logscale_factor)
    return x