code: def forward(self, input_pts, input_views=None):
      assert input_pts.shape[-1] == self.input_ch
      if not self.use_viewdirs:
          input_views = None
      if input_views is not None:
          assert input_views.shape[-1] == self.input_ch_views
      h = input_pts
      for i, l in enumerate(self.pts_linears):
          h = self.pts_linears[i](h)
          h = F.relu(h)
          if i in self.skips:
              h = torch.cat([input_pts, h], -1)
  
      if self.use_viewdirs:
          assert input_views is not None
          alpha = self.alpha_linear(h)
          feature = self.feature_linear(h)
          h = torch.cat([feature, input_views], -1)
  
          for i, l in enumerate(self.views_linears):
              h = self.views_linears[i](h)
              h = F.relu(h)
  
          rgb = self.rgb_linear(h)
          outputs = torch.cat([rgb, alpha], -1)
      else:
          outputs = self.output_linear(h)
      if self.scale_type == "no":
          return outputs
      elif self.scale_type == "linear":
          return outputs * self.scale
      elif self.scale_type == "tanh":
          return torch.tanh(outputs) * self.scale
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import numpy
label_desc: Use image node_id 2 for calling the NeRF obj's underlying member method code with example usage: obj.forward(input_pts, input_views) and returns: outputs, unknown, unknown