code: def __init__(
      self,
      d_hidden: int = 256,
      n_output: int = 12,
      n_hidden_layers: int = 6,
      act_fn: str = "swish",
      insert_direction_at: int = 4,
  ):
      super().__init__()
  
      # Instantiate the MLP
  
      # Find out the dimension of encoded position and direction
      dummy = torch.eye(1, 3)
      d_posenc_pos = encode_position(position=dummy).shape[-1]
      d_posenc_dir = encode_direction(position=dummy).shape[-1]
  
      mlp_widths = [d_hidden] * n_hidden_layers
      input_widths = [d_posenc_pos] + mlp_widths
      output_widths = mlp_widths + [n_output]
  
      if insert_direction_at is not None:
          input_widths[insert_direction_at] += d_posenc_dir
  
      self.mlp = nn.ModuleList(
          [
              nn.Linear(d_in, d_out)
              for d_in, d_out in zip(input_widths, output_widths)
          ]
      )
  
      if act_fn == "swish":
          # self.activation = swish
          # yiyi testing:
          self.activation = lambda x: F.silu(x)
      else:
          raise ValueError(f"Unsupported activation function {act_fn}")
  
      self.sdf_activation = torch.tanh
      self.density_activation = torch.nn.functional.relu
      self.channel_activation = torch.sigmoid
  
imports: import math
  from dataclasses import dataclass
  from typing import Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  from ...utils import BaseOutput
  from .camera import create_pan_cameras
label_desc: Use image node_id 1 to create a new MLPNeRSTFModel object from inherited base classes: ModelMixin, ConfigMixin with example: obj = MLPNeRSTFModel(d_hidden, n_output, n_hidden_layers, act_fn, insert_direction_at)