code: def forward(
      self, x: torch.Tensor, t: Optional[torch.Tensor] = None
  ) -> torch.Tensor:
      if len(x.shape) != 4:
          raise ValueError("input should be 4d")
      if x.shape[1] != 3:
          raise ValueError("input should have 3 channels")
      if not (
          x.shape[2] == self.image_size
          and x.shape[3] == self.image_size
      ):
          raise ValueError(
              f"input is not {self.image_size} x {self.image_size}"
          )
  
      if (self.n_timestep == 0 and t is not None) or (
          self.n_timestep != 0 and t is None
      ):
          raise ValueError()
      if self.n_timestep != 0:
          assert t is not None
          if len(t.shape) != 1:
              raise ValueError()
          if t.shape[0] != x.shape[0]:
              raise ValueError()
  
      x = (x - self.channel_means) / self.channel_stds
      x = F.conv2d(x, self.patch_proj, stride=self.patch_size)
      x = x.reshape(
          x.shape[0],
          self.n_state,
          (self.image_size // self.patch_size) ** 2,
      ).permute(0, 2, 1)
  
      sot = (
          self.pred_state[None, None].expand(x.shape[0], -1, -1)
          if self.n_timestep == 0
          else F.embedding(cast(torch.Tensor, t), self.w_t)[:, None]
      )
      x = torch.cat((sot, x), dim=1) + self.w_pos[None]
      return self.ln(x)
  
imports: import math
  from collections import OrderedDict
  from typing import List, Optional, Tuple, cast
  import attr
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .attention import AttentionInfo, DenseAttentionMask, DenseCausalAttentionMask, make_full_layout, to_attention_info
  from .utils import Affine, LayerNorm, zero_key_bias_grad
label_desc: Use image node_id 2 for calling the ImageEmbedding obj's underlying member method code with example usage: obj.forward(x, t) and returns: self