class_docstr: A stack of FCLayers.
  
  The specification of each FCLayer is specified by the `layers` dictionary parameter, whose keys correspond with an
  FCLayer's constructor arguments, i.e.
  
  [
      {"input_size": 2, "output_size": 4},
      {"output_size": 4, "use_bias": False},
  ]
  
  `default_*` parameters dictate default values to use for each FCLayer, if not specified by `layers`. If `layers` is
  `None`, then a stack of size `num_layers` of `FCLayer`s configured with all of the `default_*` parameters is used.
  
  If `layers` is None and `num_layers` is 0, then there are no fully connected layers and this module serves as a
  trivial passthrough.
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code: def input_shape(self) -> torch.Size:
      return torch.Size([self.input_size])
  
imports: import logging
  from copy import deepcopy
  from typing import Dict, List, Optional
  import torch
  from torch.nn import Dropout, Linear, ModuleList
  from ludwig.modules.normalization_modules import create_norm_layer
  from ludwig.utils.torch_utils import activations, initializer_registry, LudwigModule
label_desc: Use image node_id 4 for calling the FCStack obj's underlying member method code with example usage: obj.input_shape() and returns: torch