code: def inspect(
      self,
      net: Tensor,
      x_dims: Tuple[int, ...],
      y_dim: int,
      *,
      already_extracted: bool = False,
  ) -> Tensor:
      try:
          idx = self.all_indices.index(x_dims)
      except ValueError:
          raise ValueError(
              f"`x_dims` ({x_dims}) is not in the pre-defined nary"
          )
      with eval_context(self):
          B = len(net)
          if len(net.shape) > 2:
              net = net.contiguous().view(B, -1)
          order = len(x_dims)
          # [B, 1, num_bases]
          indices = (
              [tuple(range(order))] if already_extracted else [x_dims]
          )
          net = self.bases[str(order)](net, indices)
          net = self.bases_dropout(net)
          # [B, num_bases]
          net = net.view(B, self.num_bases, 1).squeeze(2)
          # [B, 1]
          w = self.featurizer.weight[[idx]].squeeze(2)
          b = None if not self.bias else self.featurizer.bias[[idx]]
          net = F.linear(net, w, b)
          # [B, 1]
          w = self.head.weight[..., [idx]][[y_dim]]
          net = F.linear(net, w)
      return net
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Dict
  from typing import List
  from typing import Tuple
  from typing import Optional
  from itertools import combinations
  from .base import MLModel
  from ...schema import MLEncoderSettings
  from ...schema import MLGlobalEncoderSettings
  from ...misc.toolkit import eval_context
  from ...modules.blocks import Activation
label_desc: Use image node_id 4 for calling the NBM obj's underlying member method code with example usage: obj.inspect(net, x_dims, y_dim) and returns: net