code: def basic_blocks(
      dim: int,
      block_index: int,
      num_blocks: List[int],
      token_mixer_type: str,
      kernel_size: int = 3,
      mlp_ratio: float = 4.0,
      act_layer: nn.Module = nn.GELU,
      norm_layer: nn.Module = nn.BatchNorm2d,
      drop_rate: float = 0.0,
      drop_path_rate: float = 0.0,
      use_layer_scale: bool = True,
      layer_scale_init_value: float = 1e-5,
      inference_mode=False,
  ) -> nn.Sequential:
      """Build FastViT blocks within a stage.
  
      Args:
          dim: Number of embedding dimensions.
          block_index: block index.
          num_blocks: List containing number of blocks per stage.
          token_mixer_type: Token mixer type.
          kernel_size: Kernel size for repmixer.
          mlp_ratio: MLP expansion ratio.
          act_layer: Activation layer.
          norm_layer: Normalization layer.
          drop_rate: Dropout rate.
          drop_path_rate: Drop path rate.
          use_layer_scale: Flag to turn on layer scale regularization.
          layer_scale_init_value: Layer scale value at initialization.
          inference_mode: Flag to instantiate block in inference mode.
  
      Returns:
          nn.Sequential object of all the blocks within the stage.
      """
      blocks = []
      for block_idx in range(num_blocks[block_index]):
          block_dpr = (
              drop_path_rate
              * (block_idx + sum(num_blocks[:block_index]))
              / (sum(num_blocks) - 1)
          )
          if token_mixer_type == "repmixer":
              blocks.append(
                  RepMixerBlock(
                      dim,
                      kernel_size=kernel_size,
                      mlp_ratio=mlp_ratio,
                      act_layer=act_layer,
                      drop=drop_rate,
                      drop_path=block_dpr,
                      use_layer_scale=use_layer_scale,
                      layer_scale_init_value=layer_scale_init_value,
                      inference_mode=inference_mode,
                  )
              )
          elif token_mixer_type == "attention":
              blocks.append(
                  AttentionBlock(
                      dim,
                      mlp_ratio=mlp_ratio,
                      act_layer=act_layer,
                      norm_layer=norm_layer,
                      drop=drop_rate,
                      drop_path=block_dpr,
                      use_layer_scale=use_layer_scale,
                      layer_scale_init_value=layer_scale_init_value,
                  )
              )
          else:
              raise ValueError(
                  "Token mixer type: {} not supported".format(
                      token_mixer_type
                  )
              )
      blocks = nn.Sequential(*blocks)
  
      return blocks
  
imports: import os
  import copy
  from functools import partial
  from typing import List, Tuple, Optional, Union
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.models.layers import DropPath, trunc_normal_
  from timm.models.registry import register_model
  from models.modules.mobileone import MobileOneBlock
  from models.modules.replknet import ReparamLargeKernelConv
label_desc: Use image node_id 3 for calling a global function with example usage: basic_blocks(dim, block_index, num_blocks, token_mixer_type, kernel_size, mlp_ratio, act_layer, norm_layer, drop_rate, drop_path_rate, use_layer_scale, layer_scale_init_value, inference_mode) and returns: blocks