code: def __init__(
      self, input_dim, embed_dim, cube_size, patch_size=16, dropout=0.1
  ):
      super().__init__()
      self.n_patches = int(
          (cube_size[0] * cube_size[1] * cube_size[2])
          / (patch_size * patch_size * patch_size)
      )
      self.patch_size = patch_size
      self.embed_dim = embed_dim
      self.patch_embeddings = nn.Conv3D(
          in_channels=input_dim,
          out_channels=embed_dim,
          kernel_size=patch_size,
          stride=patch_size,
          bias_attr=False,
      )
      self.position_embeddings = AbsPositionalEncoding1D(
          self.n_patches, embed_dim
      )
      self.dropout = nn.Dropout(dropout)
  
imports: from typing import List, Tuple
  import numpy
  import paddle
  import paddle.nn
  from paddle import Tensor
  from medicalseg.cvlibs import manager
label_desc: Use image node_id 1 to create a new Embeddings3D object from inherited base classes: nn with example: obj = Embeddings3D(input_dim, embed_dim, cube_size, patch_size, dropout)