class_docstr: This ``Module`` is a feed-forward neural network, just a sequence of ``Linear`` layers with
  activation functions in between.
  
  Parameters
  ----------
  input_dim : ``int``
      The dimensionality of the input.  We assume the input has shape ``(batch_size, input_dim)``.
  num_layers : ``int``
      The number of ``Linear`` layers to apply to the input.
  hidden_dims : ``Union[int, Sequence[int]]``
      The output dimension of each of the ``Linear`` layers.  If this is a single ``int``, we use
      it for all ``Linear`` layers.  If it is a ``Sequence[int]``, ``len(hidden_dims)`` must be
      ``num_layers``.
  activations : ``Union[Callable, Sequence[Callable]]``
      The activation function to use after each ``Linear`` layer.  If this is a single function,
      we use it after all ``Linear`` layers.  If it is a ``Sequence[Callable]``,
      ``len(activations)`` must be ``num_layers``.
  dropout : ``Union[float, Sequence[float]]``, optional
      If given, we will apply this amount of dropout after each layer.  Semantics of ``float``
      versus ``Sequence[float]`` is the same as with other parameters.
code: def get_input_dim(self):
      return self.input_dim
  
file_path: magnitude/pymagnitude/third_party/allennlp/modules/feedforward.py
imports: from __future__ import absolute_import
  import torch
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.nn import Activation
label_desc: Use image node_id 3 for calling the FeedForward obj's underlying member method code with example usage: obj.get_input_dim() and returns: self