code: def forward(self, input_tensor, temb):
      shape = input_tensor.shape
      n_dim = len(self.channels_multidim)
      input_tensor = input_tensor.reshape(
          *shape[0:-n_dim], self.in_channels_prod, 1, 1
      )
      input_tensor = input_tensor.view(-1, self.in_channels_prod, 1, 1)
  
      hidden_states = input_tensor
  
      hidden_states = self.norm1(hidden_states)
      hidden_states = self.nonlinearity(hidden_states)
      hidden_states = self.conv1(hidden_states)
  
      if temb is not None:
          temb = self.time_emb_proj(self.nonlinearity(temb))[
              :, :, None, None
          ]
          hidden_states = hidden_states + temb
  
      hidden_states = self.norm2(hidden_states)
      hidden_states = self.nonlinearity(hidden_states)
  
      hidden_states = self.dropout(hidden_states)
      hidden_states = self.conv2(hidden_states)
  
      if self.conv_shortcut is not None:
          input_tensor = self.conv_shortcut(input_tensor)
  
      output_tensor = input_tensor + hidden_states
  
      output_tensor = output_tensor.view(*shape[0:-n_dim], -1)
      output_tensor = output_tensor.view(
          *shape[0:-n_dim], *self.out_channels_multidim
      )
  
      return output_tensor
  
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 ResnetBlockFlat obj's underlying member method code with example usage: obj.forward(input_tensor, temb) and returns: output_tensor