code: def shape_inference(
      self, func: NativeFunction, schema: LazyIrSchema
  ) -> str:
      metadata = self.backend_index.get_kernel(func)
      assert metadata is not None
      all_args = schema.filtered_args()
      returns_length = len(schema.returns)
      # call the meta kernel if it exists, to compute output shape/dtype for our IR
      # Note [Generated LTC Shape Functions]
      # LTC uses meta tensors from core to do shape inference when possible, and otherwise
      # we generate a shape function declaration that needs to be manually implemented.
      # How do we detect which ops are eligible to use meta tensors?
      # In general we should be able to use meta tensors not just on structured operators,
      # but also on composite operators that are implemented in terms of structured kernels.
      # We don't currently have a way of knowing at codegen time which ops are implemented that way.
      # This is the case for all view and view_copy operators however, so we're going to
      # use them specifically for all of the view_copy ops (instead of manually writing shape rules for all of them).
      is_view_copy_op = "view_copy" in func.tags
      is_structured = (
          func.structured or func.structured_delegate is not None
      )
      if is_structured or is_view_copy_op:
          meta_out = """
  std::vector<torch::lazy::Shape> shapes{torch::lazy::Shape(out_meta.scalar_type(), out_meta.sizes().vec())};"""
          if returns_length > 1:
  
              def this_shape(i: int) -> str:
                  return f"torch::lazy::Shape(std::get<{i}>(out_meta).scalar_type(), std::get<{i}>(out_meta).sizes().vec())"
  
              shapes_str = ",".join(
                  [this_shape(i) for i in range(returns_length)]
              )
              meta_out = (
                  "std::vector<torch::lazy::Shape> shapes{"
                  + shapes_str
                  + "};"
              )
  
          # Convert tensor args to the meta device and call it.
          # (We can't pass in the input tensors directly, because they are "functional wrappers".
          # If any of the meta kernels call a tensor op and redispatch, we don't want to hit the functionalize kernels.)
          # Even at::meta:: functions might redispatch, e.g. if they call into view ops.
          dispatcher_sig = DispatcherSignature.from_schema(func.func)
          meta_conversion_str, meta_call_ctx = convert_to_meta_tensors(
              dispatcher_sig
          )
          meta_call_args = [
              e.expr
              for e in translate(
                  meta_call_ctx,
                  dispatcher_sig.arguments(),
                  method=False,
              )
          ]
          if is_view_copy_op:
              # view_copy ops always have a CompositeExplicitAutogradNonFunctional kernel
              assert (
                  func.has_composite_explicit_autograd_non_functional_kernel
              )
              dispatch_ns = "compositeexplicitautogradnonfunctional"
          else:
              dispatch_ns = "meta"
          aten_name = schema.aten_name
          # TODO: this is trolling
          if func.func.has_symint() and metadata.supports_symint():
              aten_name += "_symint"
          shape_str = f"""\
          {meta_conversion_str}
          auto out_meta = at::{dispatch_ns}::{aten_name}({', '.join(meta_call_args)});
          {meta_out}"""
      else:
          shape_sig = ComputeShapeSignature(
              metadata.kernel, func, symint=metadata.supports_symint()
          )
          shape_str = f"""
              auto shapes = {shape_sig.shape_call};"""
  
      shape_str += f"""
              TORCH_INTERNAL_ASSERT(shapes.size() == {returns_length});"""
  
      # Calculating which dimensions are symbolic
      func_schema_str = "aten::" + str(func.func)
      shape_str += f"""
              if(torch::lazy::symbolicShapeEnabled()){{
                  std::vector<torch::jit::IValue> inputs = {{ {', '.join(str(a.name) for a in all_args)} }};
                  const char* schema_str = "{func_schema_str}";
                  applySymbolicShapesOnLT(schema_str, inputs, shapes);
              }}
          """
      return shape_str
  
imports: import itertools
  from abc import ABC
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torchgen.api.dispatcher
  from torchgen.api.lazy import getValueT, isValueType, LazyArgument, LazyIrProperties, LazyIrSchema, tensorListValueT
  from torchgen.api.translate import translate
  from torchgen.api.types import BaseCType, Binding, deviceT, DispatcherSignature, kernel_signature, NativeSignature, OptionalCType, VectorCType
  from torchgen.context import method_with_native_function
  from torchgen.dest.lazy_ts_lowering import ts_lowering_body
  from torchgen.model import Argument, BackendIndex, BackendMetadata, BaseTy, BaseType, FunctionSchema, ListType, NativeFunction, NativeFunctionsGroup
label_desc: Use image node_id 5 for calling the GenLazyNativeFuncDefinition obj's underlying member method code with example usage: obj.shape_inference(func, schema) and returns: shape_str, str, i, str, i, str