code: def forward(
      self,
      boxes,
      masks,
      positive_embeddings=None,
      phrases_masks=None,
      image_masks=None,
      phrases_embeddings=None,
      image_embeddings=None,
  ):
      masks = masks.unsqueeze(-1)
  
      xyxy_embedding = self.fourier_embedder(boxes)
      xyxy_null = self.null_position_feature.view(1, 1, -1)
      xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null
  
      if positive_embeddings:
          positive_null = self.null_positive_feature.view(1, 1, -1)
          positive_embeddings = (
              positive_embeddings * masks + (1 - masks) * positive_null
          )
  
          objs = self.linears(
              torch.cat([positive_embeddings, xyxy_embedding], dim=-1)
          )
      else:
          phrases_masks = phrases_masks.unsqueeze(-1)
          image_masks = image_masks.unsqueeze(-1)
  
          text_null = self.null_text_feature.view(1, 1, -1)
          image_null = self.null_image_feature.view(1, 1, -1)
  
          phrases_embeddings = (
              phrases_embeddings * phrases_masks
              + (1 - phrases_masks) * text_null
          )
          image_embeddings = (
              image_embeddings * image_masks
              + (1 - image_masks) * image_null
          )
  
          objs_text = self.linears_text(
              torch.cat([phrases_embeddings, xyxy_embedding], dim=-1)
          )
          objs_image = self.linears_image(
              torch.cat([image_embeddings, xyxy_embedding], dim=-1)
          )
          objs = torch.cat([objs_text, objs_image], dim=1)
  
      return objs
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from ...configuration_utils import ConfigMixin, register_to_config
  from ...models import ModelMixin
  from ...models.activations import get_activation
  from ...models.attention import Attention
  from ...models.attention_processor import ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnAddedKVProcessor2_0, AttnProcessor
  from ...models.dual_transformer_2d import DualTransformer2DModel
  from ...models.embeddings import GaussianFourierProjection, ImageHintTimeEmbedding, ImageProjection, ImageTimeEmbedding, TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
  from ...models.transformer_2d import Transformer2DModel
  from ...models.unet_2d_condition import UNet2DConditionOutput
  from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import apply_freeu
label_desc: Use image node_id 2 for calling the PositionNet obj's underlying member method code with example usage: obj.forward(boxes, masks, positive_embeddings, phrases_masks, image_masks, phrases_embeddings, image_embeddings) and returns: objs