code: def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.0):
      super().__init__()
      self.layers = nn.ModuleList([])
      for _ in range(depth):
          self.layers.append(
              nn.ModuleList(
                  [
                      Attention(
                          dim,
                          heads=heads,
                          dim_head=dim_head,
                          dropout=dropout,
                      ),
                      FeedForward(dim, mlp_dim, dropout=dropout),
                  ]
              )
          )
  
file_path: vit-pytorch/vit_pytorch/vit_1d.py
imports: import torch
  from torch import nn
  from einops import rearrange, repeat, pack, unpack
  from einops.layers.torch import Rearrange
label_desc: Use image node_id 1 to create a new Transformer object from inherited base classes: nn with example: obj = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)