class_docstr: SpatialFc module that take features with shape of (B,C,*) as input.
  Args:
      spatial_shape (tuple[int]): spatial shape
code: def __init__(self, spatial_shape=[14, 14]):
      super().__init__()
      if isinstance(spatial_shape, int):
          spatial_shape = [spatial_shape]
      assert isinstance(spatial_shape, Sequence), (
          f'"spatial_shape" must by a sequence or int, '
          f"get {type(spatial_shape)} instead."
      )
      n = reduce(lambda x, y: x * y, spatial_shape)
      self.fc = nn.Linear(n, n, bias=False)
  
imports: from torch import nn
  import torch
  from typing import Sequence
  from functools import partial, reduce
label_desc: Use image node_id 1 to create a new SpatialFc object from inherited base classes: nn with example: obj = SpatialFc(spatial_shape)