code: def __init__(
      self,
      dim,
      num_scales=1,
      num_heads=8,
      expand_ratio=4,
      use_pos_emb=True,
      conv_bias=True,
      qkv_bias=True,
      ls_init_value=1e-6,
      norm_layer=partial(nn.LayerNorm, eps=1e-6),
      act_layer=nn.GELU,
      drop_path=0.0,
      attn_drop=0.0,
      proj_drop=0.0,
  ):
      super().__init__()
      width = max(
          int(math.ceil(dim / num_scales)),
          int(math.floor(dim // num_scales)),
      )
      self.width = width
      self.num_scales = max(1, num_scales - 1)
  
      convs = []
      for i in range(self.num_scales):
          convs.append(
              create_conv2d(
                  width,
                  width,
                  kernel_size=3,
                  depthwise=True,
                  bias=conv_bias,
              )
          )
      self.convs = nn.ModuleList(convs)
  
      self.pos_embd = None
      if use_pos_emb:
          self.pos_embd = PositionalEncodingFourier(dim=dim)
      self.norm_xca = norm_layer(dim)
      self.gamma_xca = (
          nn.Parameter(ls_init_value * torch.ones(dim))
          if ls_init_value > 0
          else None
      )
      self.xca = CrossCovarianceAttn(
          dim,
          num_heads=num_heads,
          qkv_bias=qkv_bias,
          attn_drop=attn_drop,
          proj_drop=proj_drop,
      )
  
      self.norm = norm_layer(dim, eps=1e-6)
      self.mlp = Mlp(dim, int(expand_ratio * dim), act_layer=act_layer)
      self.gamma = (
          nn.Parameter(ls_init_value * torch.ones(dim))
          if ls_init_value > 0
          else None
      )
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
imports: import math
  from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_tf_, DropPath, LayerNorm2d, Mlp, SelectAdaptivePool2d, create_conv2d, use_fused_attn, NormMlpClassifierHead, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_module
  from ._manipulate import named_apply, checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new SplitTransposeBlock object from inherited base classes: nn with example: obj = SplitTransposeBlock(dim, num_scales, num_heads, expand_ratio, use_pos_emb, conv_bias, qkv_bias, ls_init_value, norm_layer, act_layer, drop_path, attn_drop, proj_drop)