class_docstr: Generates model graph, each node is created from single or multiple jit trace nodes.
class_docstr_tok: ["Generates","model","graph",",","each","node","is","created","from","single","or","multiple","jit","trace","nodes","."]
docstr: Extract the shape information of ```aten::view``` node
  
  Parameters
  ----------
  node : trace graph node
      It should be ```aten::view``` node
  
  Returns
  -------
  dict
      Include shape of input tensor and shape of output tensor
docstr_tok: ["Extract","the","shape","information","of","``","`","aten",":",":view","``","`","node","Parameters","--","--","--","--","--","node",":","trace","graph","node","It","should","be","``","`","aten",":",":view","``","`","node","Returns","--","--","--","-","dict","Include","shape","of","input","tensor","and","shape","of","output","tensor"]
code: def _extract_shape_info(self, node):
      """
      Extract the shape information of ```aten::view``` node
  
      Parameters
      ----------
      node : trace graph node
          It should be ```aten::view``` node
  
      Returns
      -------
      dict
          Include shape of input tensor and shape of output tensor
      """
      t_input = None
      for _input in node.inputs():
          t_input = _input
          break
      t_output = node.output()
      assert isinstance(t_input.type(), torch._C.TensorType)
      assert isinstance(t_output.type(), torch._C.TensorType)
      in_shape = t_input.type().sizes()
      out_shape = t_output.type().sizes()
      return {"in_shape": in_shape, "out_shape": out_shape}
  
imports: import logging
  import queue
  import re
  from collections import defaultdict
  from typing import List, Dict
  import torch
  from torch.utils.tensorboard._pytorch_graph import NodePy, NodePyIO, NodePyOP, GraphPy
label_desc: Use image node_id 6 for calling the TorchModuleGraph obj's underlying member method code with example usage: obj._extract_shape_info(node) and returns: dict