code: def __call__(self, g: NativeFunctionsViewGroup) -> Optional[str]:
      if g.view_copy is None:
          return None
      elif (
          g.view_copy.func.name.name.base
          != f"{g.view.func.name.name}_copy"
      ):
          # If the view_copy doesn't match the standard naming scheme of <op>_copy,
          # assume it already exists and doesn't need to be generated.
          # Example: slice_inverse() with the copy variant named slice_scatter()
          # instead of slice_inverse_copy()
          return None
  
      metadata = self.backend_index.get_kernel(g.view_copy)
      assert metadata is not None
  
      # We can make view_copy work in more cases by using reshape()
      # when a normal view call would ordinarily fail.
      # This also makes LTC more efficient, because they don't need to include
      # clone() calls in their graph (which is normally needed by reshape).
      if str(g.view_copy.func.name) == "view_copy":
          assert metadata.kernel == "view_copy_symint"
          return """\
  at::Tensor view_copy_symint(const at::Tensor & self, at::SymIntArrayRef size) {
    c10::SymDimVector shape = infer_size_dv(size, self.sym_numel());
    if (!at::detail::computeStride(self.sym_sizes(), self.sym_strides(), shape).has_value()) {
      return self.reshape_symint(size);
    } else {
      auto output = at::_ops::view::call(self, size);
      return output.clone(/*memory_format=*/at::MemoryFormat::Contiguous);
    }
  }
  """
      # view_copy is a native signature, since we're generating an at::native:: kernel
      # Functionalization always operates on symints though
      view_copy_sig = NativeSignature(
          g.view_copy.func, symint=metadata.supports_symint()
      )
  
      # view is a dispatcher signature, since we're calling into the at::_ops API
      view_sig = DispatcherSignature(g.view.func)
  
      view_api_name = g.view.func.name.unambiguous_name()
      exprs = ", ".join(
          [
              e.expr
              for e in translate(
                  view_copy_sig.arguments(), view_sig.arguments()
              )
          ]
      )
  
      # view ops today always return either a Tensor or a list of Tensors
      assert len(g.view.func.returns) == 1
      assert g.view.func.returns[0].type == BaseType(
          BaseTy.Tensor
      ) or g.view.func.returns[0].type == ListType(
          BaseType(BaseTy.Tensor), None
      )
  
      if g.view.func.returns[0].type == BaseType(BaseTy.Tensor):
          return_cloned_output = """\
    return output.clone(/*memory_format=*/at::MemoryFormat::Contiguous);"""
      else:
          # If the return type is a list, we need to clone each tensor in the list.
          return_cloned_output = f"""\
    {view_copy_sig.returns_type().cpp_type()} out_clone;
    for (const auto i : c10::irange(output.size())) {{
      out_clone.push_back(output[i].clone(/*memory_format=*/at::MemoryFormat::Contiguous));
    }}
    return out_clone;"""
  
      # The default generated composite kernel for {view}_copy() operators just clones
      # the input tensor, and runs the underlying view on the clone.
      return f"""
  {view_copy_sig.defn(name=metadata.kernel)} {{
    auto output = at::_ops::{view_api_name}::call({exprs});
    {return_cloned_output}
  }}
  """
  
imports: from dataclasses import dataclass
  from typing import Callable, List, Optional, Tuple, Union
  from torchgen.api import cpp, dispatcher
  from torchgen.api.translate import translate
  from torchgen.api.types import BaseCType, Binding, CType, DispatcherSignature, FunctionalizationLambda, iTensorListRefT, NativeSignature, tensorListT, tensorT, VectorCType, ViewInverseSignature
  from torchgen.context import method_with_native_function, native_function_manager, with_native_function, with_native_function_and
  from torchgen.model import Argument, BackendIndex, BaseTy, BaseType, FunctionSchema, ListType, NativeFunction, NativeFunctionsGroup, NativeFunctionsViewGroup, Return, SchemaKind, SelfArgument, TensorOptionsArguments
  from torchgen.native_function_generation import INPLACE_OPS_THAT_DONT_GET_GROUPED_PROPERLY, MUTABLE_OPS_THAT_CANNOT_GET_AN_OUT_VARIANT, OUT_OPS_THAT_DONT_GET_GROUPED_PROPERLY
  from torchgen.selective_build.selector import SelectiveBuilder
label_desc: Use image node_id 1 for calling the GenCompositeViewCopyKernel obj's underlying member method code with example usage: obj.__call__(g) and returns: str, view_copy_sig, str, view_api_name, str, exprs, str, return_cloned_output, str, None, str, None