code: def pad(x, width, val=0, batch_ndim=1):
      """
      Pad a tensor with a constant value.
  
      Parameters
      ----------
      x : tensor
  
      width : int, iterable of int, or iterable of tuple
          Padding width. If an int, pads each axis symmetrically with the same
          amount in the beginning and end. If an iterable of int, defines the
          symmetric padding width separately for each axis. If an iterable of
          tuples of two ints, defines a seperate padding width for each beginning
          and end of each axis.
  
      val : float
          The constant value used for padding
  
      batch_ndim : integer
          Dimensions before the value will not be padded.
  
      """
      input_shape = x.shape
      input_ndim = x.ndim
  
      output_shape = list(input_shape)
      indices = [slice(None) for _ in output_shape]
  
      if isinstance(width, int_types):
          widths = [width] * (input_ndim - batch_ndim)
      else:
          widths = width
  
      for k, w in enumerate(widths):
          try:
              l, r = w
          except TypeError:
              l = r = w
          output_shape[k + batch_ndim] += l + r
          indices[k + batch_ndim] = slice(
              l, l + input_shape[k + batch_ndim]
          )
  
      if val:
          out = T.ones(output_shape) * val
      else:
          out = T.zeros(output_shape)
      return T.set_subtensor(out[tuple(indices)], x)
  
label_desc: Use image node_id 1 for calling a global function with example usage: pad(x, width, val, batch_ndim) and returns: T