code: def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, Dict]:
      rearr = False
      in_shape = z.shape
  
      if z.ndim > 3:
          rearr = self.output_dim is not None
          z = rearrange(z, "b c ... -> b (...) c")
      z = self.proj_in(z)
      z_q, loss_dict = super().forward(z)
  
      z_q = self.proj_out(z_q)
      if rearr:
          if len(in_shape) == 4:
              z_q = rearrange(
                  z_q, "b (h w) c -> b c h w ", w=in_shape[-1]
              )
          elif len(in_shape) == 5:
              z_q = rearrange(
                  z_q,
                  "b (t h w) c -> b c t h w ",
                  w=in_shape[-1],
                  h=in_shape[-2],
              )
          else:
              raise NotImplementedError(
                  f"rearranging not available for {len(in_shape)}-dimensional input."
              )
  
      return z_q, loss_dict
  
imports: import logging
  from abc import abstractmethod
  from typing import Dict, Iterator, Literal, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange
  from torch import einsum
  from .base import AbstractRegularizer, measure_perplexity
label_desc: Use image node_id 2 for calling the VectorQuantizerWithInputProjection obj's underlying member method code with example usage: obj.forward(z) and returns: z_q,  loss_dict