code: def gen_one(self, f: NativeFunction) -> Optional[str]:
      assert not f.manual_kernel_registration
  
      if (
          self.target is Target.REGISTRATION
          and not self.selector.is_native_function_selected(f)
      ):
          return None
  
      # TODO: Now, there is something interesting going on here.  In the code below,
      # we generate CompositeExplicitAutogradNonFunctional implementations of functional and inplace
      # based on the out implementation.  But in fact, out is definable by
      # functional too (just not very efficiently), and this is honestly the
      # MORE likely situation for a backend implementor.  How do we pick?
      # Well, taking a page from Haskell type classes and default methods,
      # we could conceivably register a circular definition (out in terms
      # of functional, and functional in terms of out) and just require
      # someone to implement one or the other.  We'd have to do a little bit
      # of work to not register one of these "weak" definitions unless there
      # is a strong definition somewhere in the DAG!  So it's not implemented yet.
      if (
          self.backend_index.dispatch_key
          == DispatchKey.CompositeExplicitAutogradNonFunctional
          and f.func.kind() is SchemaKind.out
      ):
          # Never generate a default implementation for out, that's what you
          # have to define as a backend implementor
          return None
  
      # Note [Direct dispatch bindings]
      # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      # Signature of the non-dispatched function we'll expose in a header
      # (e.g., at::cpu::add).  We don't generate methods (TODO: do this
      # when CPUTensor class is a thing); nor do we generate fallback
      # bindings for manual_cpp_binding functions.
      cpp_sig_group = CppSignatureGroup.from_native_function(
          f, method=False, fallback_binding=False
      )
  
      # Signature of the wrapper function we'll register to the dispatcher
      kern = self.backend_index.get_kernel(f)
      sig = NativeSignature(
          f.func,
          prefix=f"wrapper_{self.backend_index.dispatch_key}_",
          symint=kern is not None and kern.supports_symint(),
      )
  
      if self.target is Target.NAMESPACED_DECLARATION:
          result = ""
          for cpp_sig in cpp_sig_group.signatures(symint=self.symint):
              result += f"TORCH_API {cpp_sig.decl()};\n"
          return result
  
      elif self.target is Target.NAMESPACED_DEFINITION:
  
          def generate_defn(cpp_sig: CppSignature) -> str:
              return f"""
  {cpp_sig.defn()} {{
  return {sig.name()}({', '.join(e.expr for e in translate(cpp_sig.arguments(), sig.arguments()))});
  }}
  """
  
          result = ""
          for cpp_sig in cpp_sig_group.signatures(symint=self.symint):
              result += generate_defn(cpp_sig)
          return result
  
      elif self.target is Target.ANONYMOUS_DEFINITION:
          k = f.func.kind()
  
          # Construct the body of the wrapper function with signature sig
          sig_body = []
          # We'll use context to keep track of any variables we've brought
          # into scope while generating code
          context: List[Union[Binding, Expr]] = list(sig.arguments())
  
          # Initialize the class corresponding to this structured
          # operator; feeding it the output argument(s) if it is known
          if self.backend_index.dispatch_key is DispatchKey.Meta:
              class_name = (
                  f"structured_{meta.name(self.g)}_meta_{k.name}"
              )
              parent_class = f"at::meta::structured_{meta.name(self.g)}"
          elif (
              self.backend_index.dispatch_key
              is DispatchKey.CompositeExplicitAutogradNonFunctional
          ):
              # TODO: dedup this branch
              class_name = f"structured_{meta.name(self.g)}_default_backend_{k.name}"
              parent_class = f"at::meta::structured_{meta.name(self.g)}"
          else:
              metadata = self.backend_index.get_kernel(self.g)
              assert metadata is not None
              class_name = f"structured_{metadata.kernel}_{k.name}"
              parent_class = f"{metadata.cpp_namespace}::structured_{metadata.kernel}"
  
          if self.backend_index.device_guard:
              device_check_args = itertools.chain(
                  f.func.arguments.out, f.func.arguments.flat_positional
              )
              sig_body.append(
                  RegisterDispatchKey.gen_device_check(
                      f.device_check,
                      list(device_check_args),
                      sig.name(),
                  )
              )
  
          if k is SchemaKind.functional:
              sig_body.append(f"{class_name} op;")
          elif k is SchemaKind.inplace:
              sig_body.append(f"{class_name} op(self);")
          elif k is SchemaKind.out:
              out_args_str = ", ".join(
                  a.name for a in f.func.arguments.out
              )
              sig_body.append(f"{class_name} op({out_args_str});")
  
          # Translate the input native arguments into structured
          # arguments for the meta call
          meta_exprs = ", ".join(
              e.expr
              for e in translate(
                  context,
                  structured.meta_arguments(self.g),
                  method=False,
              )
          )
  
          if self.g.out.precomputed:
              # If this function group has precomputed elements, the meta function
              # returns a struct containing them which must be saved so that it
              # can be unpacked when generating code to call the impl.
              sig_body.append(
                  f"auto precompute = op.meta({meta_exprs});"
              )
  
              # Put all of the contents of the precompute struct into the context
              # so that translate will be able to return the correct args for the
              # call to the impl.
              precomputed_values = [
                  *self.g.out.precomputed.replace.values(),
                  self.g.out.precomputed.add,
              ]
              for precomputed_elems in precomputed_values:
                  for arg in precomputed_elems:
                      context.append(
                          Expr(
                              expr=f"precompute.{arg.name}",
                              type=structured.argument_type(
                                  arg, binds=arg.name
                              ),
                          )
                      )
  
              # Add a use of the precompute struct so FB internal compilers don't
              # complain that there is an unused variable.
              sig_body.append("(void)precompute;")
          else:
              sig_body.append(f"op.meta({meta_exprs});")
  
          # After running meta, op.outputs_ is guaranteed to be valid;
          # add it to the context
          out_args = structured.out_arguments(self.g)
          for i, out_arg in enumerate(out_args):
              assert (
                  ConstRefCType(BaseCType(tensorT))
                  == out_arg.nctype.type
              )
  
              if k is SchemaKind.out:
                  expr = f"op.maybe_get_output({i})"
              else:
                  expr = f"op.outputs_[{i}]"
  
              context.append(
                  Expr(
                      expr=expr,
                      # TODO: Stop hardcoding that the output type is a Tensor.  Note
                      # that for the codegen here this is fine because outputs_ is
                      # hardcoded to be tensor already
                      type=NamedCType(
                          out_arg.nctype.name,
                          MutRefCType(BaseCType(tensorT)),
                      ),
                  )
              )
  
          # With the expanded context, do the impl call (if not a meta
          # function)
          if (
              self.backend_index.dispatch_key
              == DispatchKey.CompositeExplicitAutogradNonFunctional
          ):
              # TODO: https://github.com/pytorch/pytorch/issues/53023
              out_sig_group = CppSignatureGroup.from_native_function(
                  self.g.out,
                  method=False,
                  fallback_binding=f.manual_cpp_binding,
              )
              out_sig = out_sig_group.most_faithful_signature()
              api_name = out_sig.name()
              out_exprs = ", ".join(
                  e.expr
                  for e in translate(
                      context, out_sig.arguments(), method=False
                  )
              )
              # TODO: I think this means structured won't work with method
              # only functions (but maybe you're saved by faithful? iunno.)
              # NB: Originally I wrote this as an at::redispatch call, but
              # I got in trouble because that meant I needed a DispatchKeySet
              # in the wrapper function, which meant I needed a DispatchKeySet
              # in the DispatchKeyFunctions declarations, but the defined API
              # there does NOT permit a dispatch key set.  I think you can
              # probably unwind this by calling some function to do the TLS
              # fetch and get the DispatchKeySet when you don't have it, but
              # I didn't do it for this version
              sig_body.append(f"at::{api_name}({out_exprs});")
          elif self.backend_index.dispatch_key != DispatchKey.Meta:
              impl_exprs = ", ".join(
                  e.expr
                  for e in translate(
                      context,
                      structured.impl_arguments(self.g),
                      method=False,
                  )
              )
              sig_body.append(f"op.impl({impl_exprs});")
  
          # Go over each output, and check if there is a proxy created for it.
          # If so, copy it over to the original output.
          if k is SchemaKind.out or k is SchemaKind.inplace:
              for i in range(len(f.func.returns)):
                  sig_body.append(
                      f"if (op.proxy_outputs_[{i}].has_value()) op.outputs_[{i}].get().copy_(*op.proxy_outputs_[{i}]);"
                  )
  
          # Destructively return the final tensors
          # TODO: Do this in translate instead
          if k is SchemaKind.functional:
              if len(f.func.returns) == 1:
                  ret_expr = (
                      "std::move(op.outputs_[0])"  # small optimization
                  )
              else:
                  moved = ", ".join(
                      f"std::move(op.outputs_[{i}])"
                      for i in range(len(f.func.returns))
                  )
                  ret_expr = f"std::make_tuple({moved})"
          elif k is SchemaKind.inplace:
              ret_expr = "self"
          elif k is SchemaKind.out:
              if len(f.func.returns) == 1:
                  ret_expr = f.func.arguments.out[0].name
              else:
                  refs = ", ".join(a.name for a in f.func.arguments.out)
                  ret_expr = f"std::forward_as_tuple({refs})"
          sig_body.append(f"return {ret_expr};")
  
          sig_body_str = "\n".join(sig_body)
  
          # For an overview of what this template code looks like, see
          # https://github.com/pytorch/rfcs/pull/9
          return f"""\
  {self.gen_class(
  f, k,
  class_name=class_name,
  parent_class=parent_class,
  generate_super=self.g.out.structured_inherits is not None
  )}
  
  {sig.defn()} {{
  {sig_body_str}
  }}
  """
  
      elif self.target is Target.REGISTRATION:
          return f'm.impl("{f.func.name}", TORCH_FN({sig.name()}));'
      else:
          assert_never(self.target)
          # Silence mypy's "Missing return statement" error
          return None
  
imports: import itertools
  import textwrap
  from dataclasses import dataclass
  from typing import List, Literal, Optional, Tuple, Union
  import torchgen.api.cpp
  import torchgen.api.meta
  import torchgen.api.structured
  from torchgen.api.translate import translate
  from torchgen.api.types import BaseCType, Binding, ConstRefCType, CppSignature, CppSignatureGroup, DispatcherSignature, Expr, kernel_signature, MutRefCType, NamedCType, NativeSignature, tensorT
  from torchgen.context import method_with_native_function, native_function_manager
  from torchgen.model import Argument, BackendIndex, DeviceCheckType, DispatchKey, gets_generated_out_inplace_wrapper, is_cuda_dispatch_key, NativeFunction, NativeFunctionsGroup, SchemaKind, TensorOptionsArguments
  from torchgen.selective_build.selector import SelectiveBuilder
  from torchgen.utils import assert_never, mapMaybe, Target
label_desc: Use image node_id 5 for calling the StructuredRegisterDispatchKey obj's underlying member method code with example usage: obj.gen_one(f) and returns: None, None, result, result, str, cpp_sig, str, sig, str, str, str, self, str, sig, str, sig_body_str, str, str, f, str, sig, str, None