def make_dot(var, params=None):
  """ Produces Graphviz representation of PyTorch autograd graph
  Blue nodes are the Variables that require grad, orange are Tensors
  saved for backward in torch.autograd.Function
  Args:
    var: output Variable
    params: dict of (name, Variable) to add names to node that
      require grad (TODO: make optional)
  """
  if params is not None:
    assert isinstance(params.values()[0], Variable)
    param_map = {id(v): k for k, v in params.items()}
  
  node_attr = dict(style='filled',
           shape='box',
           align='left',
           fontsize='12',
           ranksep='0.1',
           height='0.2')
  dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12"))
  seen = set()
  
  def size_to_str(size):
    return '('+(', ').join(['%d' % v for v in size])+')'
  def add_nodes(var):
    if var not in seen:
      if torch.is_tensor(var):
        dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange')
      elif hasattr(var, 'variable'):
        u = var.variable
        name = param_map[id(u)] if params is not None else ''
        node_name = '%s\n %s' % (name, size_to_str(u.size()))
        dot.node(str(id(var)), node_name, fillcolor='lightblue')
      else:
        dot.node(str(id(var)), str(type(var).__name__))
      seen.add(var)
      if hasattr(var, 'next_functions'):
        for u in var.next_functions:
          if u[0] is not None:
            dot.edge(str(id(u[0])), str(id(var)))
            add_nodes(u[0])
      if hasattr(var, 'saved_tensors'):
        for t in var.saved_tensors:
          dot.edge(str(id(t)), str(id(var)))
          add_nodes(t)
  add_nodes(var.grad_fn)
  return dot