code: def annotate(the_type, the_value):
      """Use to give type of `the_value` in TorchScript compiler.
  
      This method is a pass-through function that returns `the_value`, used to hint TorchScript
      compiler the type of `the_value`. It is a no-op when running outside of TorchScript.
  
      Though TorchScript can infer correct type for most Python expressions, there are some cases where
      type inference can be wrong, including:
  
      - Empty containers like `[]` and `{}`, which TorchScript assumes to be container of `Tensor`
      - Optional types like `Optional[T]` but assigned a valid value of type `T`, TorchScript would assume
        it is type `T` rather than `Optional[T]`
  
      Note that `annotate()` does not help in `__init__` method of `torch.nn.Module` subclasses because it
      is executed in eager mode. To annotate types of `torch.nn.Module` attributes,
      use :meth:`~torch.jit.Annotate` instead.
  
      Example:
  
      .. testcode::
  
          import torch
          from typing import Dict
  
          @torch.jit.script
          def fn():
              # Telling TorchScript that this empty dictionary is a (str -> int) dictionary
              # instead of default dictionary type of (str -> Tensor).
              d = torch.jit.annotate(Dict[str, int], {})
  
              # Without `torch.jit.annotate` above, following statement would fail because of
              # type mismatch.
              d["name"] = 20
  
      .. testcleanup::
  
          del fn
  
      Args:
          the_type: Python type that should be passed to TorchScript compiler as type hint for `the_value`
          the_value: Value or expression to hint type for.
  
      Returns:
          `the_value` is passed back as return value.
      """
      return the_value
  
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 2 for calling a global function with example usage: annotate(the_type, the_value) and returns: the_value