code: def isinstance(obj, target_type):
      """
      Provide container type refinement in TorchScript.
  
      It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. ``List[str]``,
      ``Dict[str, List[torch.Tensor]]``, ``Optional[Tuple[int,str,int]]``. It can also
      refine basic types such as bools and ints that are available in TorchScript.
  
      Args:
          obj: object to refine the type of
          target_type: type to try to refine obj to
      Returns:
          ``bool``: True if obj was successfully refined to the type of target_type,
              False otherwise with no new type refinement
  
  
      Example (using ``torch.jit.isinstance`` for type refinement):
      .. testcode::
  
          import torch
          from typing import Any, Dict, List
  
          class MyModule(torch.nn.Module):
              def __init__(self):
                  super().__init__()
  
              def forward(self, input: Any): # note the Any type
                  if torch.jit.isinstance(input, List[torch.Tensor]):
                      for t in input:
                          y = t.clamp(0, 0.5)
                  elif torch.jit.isinstance(input, Dict[str, str]):
                      for val in input.values():
                          print(val)
  
          m = torch.jit.script(MyModule())
          x = [torch.rand(3,3), torch.rand(4,3)]
          m(x)
          y = {"key1":"val1","key2":"val2"}
          m(y)
      """
      return _isinstance(obj, target_type)
  
imports: import warnings
  from contextlib import contextmanager
  from typing import Any, Iterator
  import torch._C
  from torch._jit_internal import _Await, _drop, _IgnoreContextManager, _isinstance, _overload, _overload_method, export, Final, Future, ignore, is_scripting, unused
  from torch.jit._async import fork, wait
  from torch.jit._await import _awaitable, _awaitable_nowait, _awaitable_wait
  from torch.jit._decomposition_utils import _register_decomposition
  from torch.jit._freeze import freeze, optimize_for_inference, run_frozen_optimizations
  from torch.jit._fuser import fuser, last_executed_optimized_graph, optimized_execution, set_fusion_strategy
  from torch.jit._ir_utils import _InsertPoint
  from torch.jit._script import _ScriptProfile, _unwrap_optional, Attribute, CompilationUnit, interface, RecursiveScriptClass, RecursiveScriptModule, script, script_method, ScriptFunction, ScriptModule, ScriptWarning
  from torch.jit._serialization import jit_module_from_flatbuffer, load, save, save_jit_module_to_flatbuffer
  from torch.jit._trace import _flatten, _get_trace_graph, _script_if_tracing, _unique_state_dict, is_tracing, ONNXTracedModule, TopLevelTracedModule, trace, trace_module, TracedModule, TracerWarning, TracingCheckError
  from torch.utils import set_module
label_desc: Use image node_id 4 for calling a global function with example usage: isinstance(obj, target_type) and returns: _isinstance