class_docstr: Completely static backend that mimics part of normal backend functionality
  but restricted to be within torchscript.
code: def reduce(x: torch.Tensor, operation: str, reduced_axes: List[int]):
      if operation == "min":
          return x.amin(dim=reduced_axes)
      elif operation == "max":
          return x.amax(dim=reduced_axes)
      elif operation == "sum":
          return x.sum(dim=reduced_axes)
      elif operation == "mean":
          return x.mean(dim=reduced_axes)
      elif operation == "prod":
          for i in list(sorted(reduced_axes))[::-1]:
              x = x.prod(dim=i)
          return x
      else:
          raise NotImplementedError("Unknown reduction ", operation)
  
file_path: einops/einops/_torch_specific.py
imports: import warnings
  from typing import Dict, List, Tuple
  import torch
  from einops.einops import TransformRecipe, _reconstruct_from_shape_uncached
label_desc: Use image node_id 1 for calling the TorchJitBackend obj's underlying member method code with example usage: obj.reduce(x, operation, reduced_axes) and returns: x, x, x, x, x