code: def apply_masked_reduction_along_dim(op, input, *args, **kwargs):
      """Applies reduction op along given dimension to strided x
      elements that are valid according to mask tensor.
  
      The op is applied to each elementary slice of input with args and
      kwargs with the following constraints:
  
      1. Prior applying the op:
  
        A. if kwargs contains an item with key 'dim_position' then it is
           removed from kwargs. The value of 'dim_position' is an
           integer that describes the dim argument position: while
           typically the dim argument appears at the 0-th position of
           the op arguments (excluding input), for instance, sum(input,
           dim), then there exists reductions that have extra arguments
           prior the dim argument, for instance, norm(input, ord, dim).
  
        B. if args or kwargs contains dim or keepdim arguments, these
           will be removed or replaced with None so that the op is
           applied to elementary slice using the default dim and keepdim
           value.
  
      2. The elementary slice of the input is defined as the flattened
        slice that has no masked out elements and when op is applied,
        the result will be a scalar value (assuming keepdim=False). For
        example, an input tensor to a reduction operation op having
        dim=0 and keepdim=True argument:
  
         [[1 * 2 * *]
          [* 3 4 * 5]]
  
        (* denotes masked out elements) has the following elementary
        slices: [1, 2] and [3, 4, 5]. The result of
        apply_masked_reduction_along_dim is
  
         [[op([1, 2], *args0, **kwargs, dim=None, keepdim=False)]
          [op([3, 4, 5], *args0, **kwargs, dim=None, keepdim=False)]]
  
        where args0 is args where dim value is replased with None if
        present.
  
        Using the same example data, if the op is called with dim=(0, 1)
        and keepdim=False, there is one elementary slice: [1, 2, 3, 4,
        5]; and the corresponding result of the op is:
  
          op([1, 2, 3, 4, 5], *args0, **kwargs, dim=None, keepdim=False)
  
      3. If the elementary slice is empty, the corresponding output
        value is nan if dtype is float, otherwise, 0.  An empty
        elementary slice corresponds to fully masked-out output, so, the
        corresponding specific value of the output will not be important
        because we used masked equality check for comparing the results
        of masked operations.
      """
      # eliminate mask and dim_position keyword arguments:
      mask = kwargs.pop("mask", None)
      dim_pos = kwargs.pop("dim_position", 0)
  
      dtype = kwargs.get("dtype", input.dtype)
      if input.ndim == 0:
          # scalar input is an elementary slice
          return op(input, *args, **kwargs).to(dtype=dtype)
  
      # eliminate keepdim keyword argument if specified:
      keepdim = kwargs.pop("keepdim", False)
  
      # eliminate dim argument that may appear both as args or kwargs
      # element:
      if dim_pos < len(args):
          # dim is specified in args
          assert "dim" not in kwargs, (args, kwargs)
          dim = args[dim_pos]
          args0 = args[:dim_pos] + (None,) + args[dim_pos + 1 :]
      else:
          # dim may be specified in kwargs
          dim = kwargs.pop("dim", None)
          args0 = args
  
      # dimensions along which the reduction operation is applied:
      dim_ = torch.masked._canonical_dim(dim, input.ndim)
      # slices in product(*ranges) define all elementary slices:
      ranges: List[Any] = []
      # shape of output for the keepdim=True case:
      shape = []
      for i in range(input.ndim):
          if i in dim_:
              ranges.append((slice(None),))
              shape.append(1)
          else:
              ranges.append(range(input.shape[i]))
              shape.append(input.shape[i])
  
      # keepdim=True version of the output, filled with nan or 0:
      output = input.new_full(
          shape,
          float("nan") if dtype.is_floating_point else 0,
          dtype=dtype,
      )
  
      # apply op to all elementary slices:
      if mask is None:
          inpmask = input.new_ones([], dtype=torch.bool).expand(
              input.shape
          )
      else:
          inpmask = torch.masked._input_mask(input, mask=mask)
      for s in itertools.product(*ranges):
          # data of an elementary slice is 1D sequence and has only
          # masked-in elements:
          data = input[s].flatten()[inpmask[s].flatten().argwhere()]
          if not data.numel():
              # empty elementary slice
              continue
          output[s][0] = op(data, *args0, **kwargs)
  
      if not keepdim:
          # reshape output for the keepdim=False case
          shape = [shape[i] for i in range(len(shape)) if i not in dim_]
          output = output.reshape(shape)
      return output
  
imports: import itertools
  import torch
  from typing import List, Any
  from functools import wraps
  import unittest
  from torch.testing._internal.common_utils import skipIfTorchDynamo
  from torch.testing._internal.common_utils import TestCase, parametrize, suppress_warnings, _TestParametrizer, run_tests
  from torch.testing._internal.common_methods_invocations import op_db, SampleInput
  from torch.testing._internal.common_device_type import instantiate_device_type_tests, ops, onlyNativeDeviceTypes, precisionOverride
label_desc: Use image node_id 1 for calling a global function with example usage: apply_masked_reduction_along_dim(op, input) and returns: output, op