code: def forward(
      self,
      inp: Union[Tensor, Tuple[Tensor, ...]],
      *,
      init: Optional[Tensor] = None,
      size: Optional[int] = None,
  ) -> Tensor:
      # get mods
      if self.modulator is None:
          mods = inp
          device = inp[0].device
      else:
          mods = self.modulator(inp)
          device = inp.device  # type: ignore
      # get siren input
      if init is not None:
          net = init
      else:
          if self.grid is None and size is None:
              raise ValueError(
                  "`init` should be provided for None sized `Siren`"
              )
          else:
              if size is None:
                  grid = self.grid
              else:
                  grid = make_grid(size, self.in_dim, device)
                  if not self.keep_edge:
                      grid = grid[1:-1]
              net = grid.repeat(len(inp), 1, 1)  # type: ignore
      # forward process
      for block, mod in zip(self.blocks, mods):
          net = block(net) * insert_intermediate_dims(mod, net)
      return self.head(net)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from typing import Tuple
  from typing import Union
  from typing import Callable
  from typing import Optional
  from cftool.types import tensor_dict_type
  from ...constants import LABEL_KEY
  from ...misc.toolkit import insert_intermediate_dims
  from ...modules.blocks import Lambda
  from ...modules.blocks import Activation
  from ...modules.blocks import ChannelPadding
label_desc: Use image node_id 2 for calling the Siren obj's underlying member method code with example usage: obj.forward(inp) and returns: self