code: def __init__(self, dim, dim_out, num_layers, hidden_size=256):
      super().__init__()
  
      layers = []
      dims = (dim, *((hidden_size,) * (num_layers - 1)))
  
      for ind, (layer_dim_in, layer_dim_out) in enumerate(
          zip(dims[:-1], dims[1:])
      ):
          is_last = ind == (len(dims) - 1)
  
          layers.extend(
              [
                  nn.Linear(layer_dim_in, layer_dim_out),
                  nn.GELU() if not is_last else nn.Identity(),
              ]
          )
  
      self.net = nn.Sequential(
          *layers, L2Norm(), nn.Linear(hidden_size, dim_out)
      )
  
imports: import copy
  import random
  from functools import wraps, partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torchvision import transforms
  from einops import rearrange, reduce, repeat
label_desc: Use image node_id 1 to create a new MLP object from inherited base classes: nn with example: obj = MLP(dim, dim_out, num_layers, hidden_size)