class_docstr: The FiLM graph convolutional operator from the
  `"GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation"
  <https://arxiv.org/abs/1906.12192>`_ paper.
  
  .. math::
      \mathbf{x}^{\prime}_i = \sum_{r \in \mathcal{R}}
      \sum_{j \in \mathcal{N}(i)} \sigma \left(
      \boldsymbol{\gamma}_{r,i} \odot \mathbf{W}_r \mathbf{x}_j +
      \boldsymbol{\beta}_{r,i} \right)
  
  where :math:`\boldsymbol{\beta}_{r,i}, \boldsymbol{\gamma}_{r,i} =
  g(\mathbf{x}_i)` with :math:`g` being a single linear layer by default.
  Self-loops are automatically added to the input graph and represented as
  its own relation type.
  
  .. note::
  
      For an example of using FiLM, see `examples/gcn.py
      <https://github.com/pyg-team/pytorch_geometric/blob/master/examples/
      film.py>`_.
  
  Args:
      in_channels (int or tuple): Size of each input sample, or :obj:`-1` to
          derive the size from the first input(s) to the forward method.
          A tuple corresponds to the sizes of source and target
          dimensionalities.
      out_channels (int): Size of each output sample.
      num_relations (int, optional): Number of relations. (default: :obj:`1`)
      nn (torch.nn.Module, optional): The neural network :math:`g` that
          maps node features :obj:`x_i` of shape
          :obj:`[-1, in_channels]` to shape :obj:`[-1, 2 * out_channels]`.
          If set to :obj:`None`, :math:`g` will be implemented as a single
          linear layer. (default: :obj:`None`)
      act (callable, optional): Activation function :math:`\sigma`.
          (default: :meth:`torch.nn.ReLU()`)
      aggr (str, optional): The aggregation scheme to use
          (:obj:`"add"`, :obj:`"mean"`, :obj:`"max"`).
          (default: :obj:`"mean"`)
      **kwargs (optional): Additional arguments of
          :class:`torch_geometric.nn.conv.MessagePassing`.
  
  Shapes:
      - **input:**
        node features :math:`(|\mathcal{V}|, F_{in})` or
        :math:`((|\mathcal{V_s}|, F_{s}), (|\mathcal{V_t}|, F_{t}))`
        if bipartite,
        edge indices :math:`(2, |\mathcal{E}|)`,
        edge types :math:`(|\mathcal{E}|)`
      - **output:** node features :math:`(|\mathcal{V}|, F_{out})` or
        :math:`(|\mathcal{V_t}|, F_{out})` if bipartite
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code: def reset_parameters(self):
      super().reset_parameters()
      for lin, film in zip(self.lins, self.films):
          lin.reset_parameters()
          reset(film)
      self.lin_skip.reset_parameters()
      reset(self.film_skip)
  
file_path: pytorch_geometric/torch_geometric/nn/conv/film_conv.py
imports: import copy
  from typing import Callable, Optional, Tuple, Union
  from torch import Tensor
  from torch.nn import ModuleList, ReLU
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.dense.linear import Linear
  from torch_geometric.nn.inits import reset
  from torch_geometric.typing import Adj, OptTensor, PairTensor, SparseTensor, torch_sparse
label_desc: Use image node_id 2 for calling the FiLMConv obj's underlying member method code with example usage: obj.reset_parameters() without return types