code: def to_json(
      self,
  ):  # pylint: disable=missing-function-docstring,too-many-locals,too-many-statements,too-many-branches
      import torch  # pylint: disable=import-outside-toplevel,import-error
  
      graph = self.value
      json_graph = {
          "arguments": [],
          "nodes": [],
          "inputs": [],
          "outputs": [],
      }
      data_type_map = dict(
          [
              [torch.float16, "float16"],  # pylint: disable=no-member
              [torch.float32, "float32"],  # pylint: disable=no-member
              [torch.float64, "float64"],  # pylint: disable=no-member
              [torch.int32, "int32"],  # pylint: disable=no-member
              [torch.int64, "int64"],  # pylint: disable=no-member
          ]
      )
  
      def constant_value(node):
          if node.hasAttribute("value"):
              selector = node.kindOf("value")
              return getattr(node, selector)("value")
          return None
  
      arguments_map = {}
  
      def argument(value):
          if not value in arguments_map:
              json_argument = {}
              json_argument["name"] = str(value.unique())
              node = value.node()
              if node.kind() == "prim::GetAttr":
                  tensor, name = self._getattr(node)
                  if (
                      tensor is not None
                      and len(name) > 0
                      and isinstance(tensor, torch.Tensor)
                  ):
                      json_argument["name"] = name
                      json_argument["initializer"] = {}
                      json_tensor_shape = {
                          "dimensions": list(tensor.shape)
                      }
                      json_argument["type"] = {
                          "dataType": data_type_map[tensor.dtype],
                          "shape": json_tensor_shape,
                      }
              elif node.kind() == "prim::Constant":
                  tensor = constant_value(node)
                  if tensor and isinstance(tensor, torch.Tensor):
                      json_argument["initializer"] = {}
                      json_tensor_shape = {
                          "dimensions": list(tensor.shape)
                      }
                      json_argument["type"] = {
                          "dataType": data_type_map[tensor.dtype],
                          "shape": json_tensor_shape,
                      }
              elif value.isCompleteTensor():
                  json_tensor_shape = {
                      "dimensions": value.type().sizes()
                  }
                  json_argument["type"] = {
                      "dataType": data_type_map[value.type().dtype()],
                      "shape": json_tensor_shape,
                  }
              arguments = json_graph["arguments"]
              arguments_map[value] = len(arguments)
              arguments.append(json_argument)
          return arguments_map[value]
  
      for value in graph.inputs():
          if (
              len(value.uses()) != 0
              and value.type().kind() != "ClassType"
          ):
              json_graph["inputs"].append(
                  {
                      "name": value.debugName(),
                      "arguments": [argument(value)],
                  }
              )
      for value in graph.outputs():
          json_graph["outputs"].append(
              {
                  "name": value.debugName(),
                  "arguments": [argument(value)],
              }
          )
      constants = {}
      for node in graph.nodes():
          if node.kind() == "prim::Constant":
              constants[node] = 0
  
      lists = {}
      for node in graph.nodes():
          if node.kind() == "prim::ListConstruct":
              if all(_.node() in constants for _ in node.inputs()):
                  for _ in node.inputs():
                      constants[_.node()] += 1
                  lists[node] = 0
  
      def create_node(node):
          schema = node.schema() if hasattr(node, "schema") else None
          schema = (
              self.metadata.type(schema)
              if schema and schema != "(no schema)"
              else None
          )
          json_node = {
              "type": {
                  "name": node.kind(),
                  "category": schema["category"]
                  if schema and "category" in schema
                  else "",
              },
              "inputs": [],
              "outputs": [],
              "attributes": [],
          }
          json_graph["nodes"].append(json_node)
          for name in node.attributeNames():
              selector = node.kindOf(name)
              value = getattr(node, selector)(name)
              json_attribute = {"name": name, "value": value}
              if torch.is_tensor(value):
                  json_node["inputs"].append(
                      {"name": name, "arguments": []}
                  )
              else:
                  json_node["attributes"].append(json_attribute)
  
          for i, value in enumerate(node.inputs()):
              parameter = (
                  schema["inputs"][i]
                  if schema and i < len(schema["inputs"])
                  else None
              )
              parameter_name = (
                  parameter["name"]
                  if parameter and "name" in parameter
                  else "input"
              )
              parameter_type = (
                  parameter["type"]
                  if parameter and "type" in parameter
                  else None
              )
              input_node = value.node()
              if input_node in constants:
                  if (
                      parameter_type == "Tensor"
                      or value.type().kind() == "TensorType"
                  ):
                      json_node["inputs"].append(
                          {
                              "name": parameter_name,
                              "arguments": [argument(value)],
                          }
                      )
                  else:
                      json_attribute = {
                          "name": parameter_name,
                          "value": constant_value(input_node),
                      }
                      if parameter and "type" in parameter:
                          json_attribute["type"] = parameter["type"]
                      json_node["attributes"].append(json_attribute)
                  constants[input_node] = constants[input_node] + 1
                  continue
              if input_node in lists:
                  json_attribute = {
                      "name": parameter_name,
                      "value": [
                          constant_value(_.node())
                          for _ in input_node.inputs()
                      ],
                  }
                  json_node["attributes"].append(json_attribute)
                  lists[input_node] += 1
                  continue
              if input_node.kind() == "prim::TupleUnpack":
                  continue
              if input_node.kind() == "prim::TupleConstruct":
                  continue
              json_node["inputs"].append(
                  {
                      "name": parameter_name,
                      "arguments": [argument(value)],
                  }
              )
  
          for i, value in enumerate(node.outputs()):
              parameter = (
                  schema["outputs"][i]
                  if schema and i < len(schema["outputs"])
                  else None
              )
              name = (
                  parameter["name"]
                  if parameter and "name" in parameter
                  else "output"
              )
              json_node["outputs"].append(
                  {"name": name, "arguments": [argument(value)]}
              )
  
      for node in graph.nodes():
          if node in lists:
              continue
          if node in constants:
              continue
          if node.kind() == "prim::GetAttr":
              continue
          create_node(node)
  
      for node in graph.nodes():
          if (
              node.kind() == "prim::Constant"
              and node in constants
              and constants[node] != len(node.output().uses())
          ):
              create_node(node)
          if (
              node.kind() == "prim::ListConstruct"
              and node in lists
              and lists[node] != len(node.output().uses())
          ):
              create_node(node)
  
      return json_graph
  
imports: import json
  import os
label_desc: Use image node_id 3 for calling the _Graph obj's underlying member method code with example usage: obj.to_json() and returns: json_graph, None, arguments_map, getattr