code: def vqvae_xcit_p8_ch64_cb1024_h8(pretrained=False, **kwargs):
      if pretrained is True:
          weights = "vqvae_xcit_p8_ch64_cb1024_h8"
      else:
          weights = None
  
      vq_model = VqVaeXCiTAutoencoder(
          ch=64,
          out_ch=3,
          ch_mult=[1, 2, 2, 4],
          num_res_blocks=2,
          attn_resolutions=[32],
          dropout=0.0,
          resamp_with_conv=True,
          in_channels=3,
          resolution=256,
          embed_dim=256,
          freeze_encoder=False,
          norm_type="channel",
          bottleneck_op=ProjectLatent(
              input_dim=256,
              output_dim=8,
              child=NormalizeLatent(
                  child=VQBottleneck(
                      codebook_size=1024, vector_length=8
                  )
              ),
          ),
      )
  
      return _build_msillm_vqvae(vq_model, weights=weights)
  
imports: import logging
  from typing import Optional
  import torch
  from neuralcompression.layers import NormalizeLatent, ProjectLatent, VQBottleneck
  from neuralcompression.models import VqVaeXCiTAutoencoder
label_desc: Use image node_id 2 for calling a global function with example usage: vqvae_xcit_p8_ch64_cb1024_h8(pretrained) and returns: _build_msillm_vqvae