code: def _avgmaxstd_reduction_function(
      x: torch.Tensor,
      w: torch.Tensor,
      reduction_functions: Sequence[ReductionFunction],
      dim: int = 1,
  ):
      """
      Args:
          x: Features to aggreagate. Tensor of shape `(batch, n_views, ..., dim)`.
          w: Aggregation weights. Tensor of shape `(batch, n_views, ...,)`.
          dim: the dimension along which to aggregate.
          reduction_functions: The set of reduction functions.
  
      Returns:
          x_aggr: Aggregation of `x` to a tensor of shape `(batch, 1, ..., dim_aggregate)`.
      """
  
      pooled_features = []
  
      mu = None
      std = None
  
      if ReductionFunction.AVG in reduction_functions:
          # average pool
          mu = _avg_reduction_function(x, w, dim=dim)
          pooled_features.append(mu)
  
      if ReductionFunction.STD in reduction_functions:
          # standard-dev pool
          std = _std_reduction_function(x, w, dim=dim, mu=mu)
          pooled_features.append(std)
  
      if ReductionFunction.STD_AVG in reduction_functions:
          # average-of-standard-dev pool
          stdavg = _std_avg_reduction_function(
              x, w, dim=dim, mu=mu, std=std
          )
          pooled_features.append(stdavg)
  
      if ReductionFunction.MAX in reduction_functions:
          max_ = _max_reduction_function(x, w, dim=dim)
          pooled_features.append(max_)
  
      # cat all results along the feature dimension (the last dim)
      x_aggr = torch.cat(pooled_features, dim=-1)
  
      # zero out features that were all masked out
      # pyre-fixme[16]: `bool` has no attribute `type_as`.
      any_active = (w.max(dim=dim, keepdim=True).values > 1e-4).type_as(
          x_aggr
      )
      x_aggr = x_aggr * any_active[..., None]
  
      # some asserts to check that everything was done right
      assert torch.isfinite(x_aggr).all()
      assert x_aggr.shape[1] == 1
  
      return x_aggr
  
imports: from abc import ABC, abstractmethod
  from enum import Enum
  from typing import Dict, Optional, Sequence, Tuple, Union
  import torch
  import torch.nn.functional
  from pytorch3d.implicitron.models.view_pooler.view_sampler import cameras_points_cartesian_product
  from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
  from pytorch3d.ops import wmean
  from pytorch3d.renderer.cameras import CamerasBase
label_desc: Use image node_id 8 for calling a global function with example usage: _avgmaxstd_reduction_function(x, w, reduction_functions, dim) and returns: x_aggr