code: def __init__(
      self,
      in_features,
      out_features=None,
      second_dim=4,
      *args,
      **kwargs,
  ):
      in_features = (
          [in_features, second_dim, 1]
          if isinstance(in_features, int)
          else list(in_features)
      )
      if out_features is None:
          out_features = in_features
      out_features = (
          [out_features, second_dim, 1]
          if isinstance(out_features, int)
          else list(out_features)
      )
      self.in_features_multidim = in_features
      self.out_features_multidim = out_features
      super().__init__(
          np.array(in_features).prod(), np.array(out_features).prod()
      )
  
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 1 to create a new LinearMultiDim object from inherited base classes: nn with example: obj = LinearMultiDim(in_features, out_features, second_dim)