code: def __init__(
      self,
      size: Optional[int],
      in_dim: int,
      out_dim: int,
      latent_dim: int,
      *,
      num_layers: int = 4,
      w_sin: float = 1.0,
      w_sin_initial: float = 30.0,
      bias: bool = True,
      final_activation: Optional[str] = None,
      keep_edge: bool = True,
      use_modulator: bool = True,
  ):
      super().__init__()
      self.in_dim = in_dim
      self.num_layers = num_layers
      self.latent_dim = latent_dim
      # siren blocks
      blocks = []
      for i in range(num_layers):
          is_first = i == 0
          i_w_sin = w_sin_initial if is_first else w_sin
          i_in_dim = in_dim if is_first else latent_dim
          blocks.append(
              SirenLayer(
                  in_dim=i_in_dim,
                  out_dim=latent_dim,
                  w_sin=i_w_sin,
                  bias=bias,
                  is_first=is_first,
              )
          )
      self.blocks = nn.ModuleList(blocks)
      # siren grid
      self.keep_edge = keep_edge
      if size is None:
          self.grid = None
      else:
          grid = make_grid(size, in_dim)
          self.register_buffer("grid", grid)
      # latent modulator
      if not use_modulator:
          self.modulator = None
      else:
          self.modulator = Modulator(latent_dim, latent_dim, num_layers)
      # head
      self.head = SirenLayer(
          in_dim=latent_dim,
          out_dim=out_dim,
          w_sin=w_sin,
          bias=bias,
          activation=Activation.make(final_activation),
      )
  
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 1 to create a new Siren object from inherited base classes: nn with example: obj = Siren(size, in_dim, out_dim, latent_dim)