class_docstr: MetaNeXtBlock Block
  Args:
      dim (int): Number of input channels.
      drop_path (float): Stochastic depth rate. Default: 0.0
      ls_init_value (float): Init value for Layer Scale. Default: 1e-6.
class_docstr_tok: ["MetaNeXtBlock","Block","Args",":","dim","(","int",")",":","Number","of","input","channels",".","drop_path","(","float",")",":","Stochastic","depth","rate",".","Default",":","0.0","ls_init_value","(","float",")",":","Init","value","for","Layer","Scale",".","Default",":","1e-6","."]
code: def __init__(
      self,
      dim,
      dilation=1,
      token_mixer=InceptionDWConv2d,
      norm_layer=nn.BatchNorm2d,
      mlp_layer=ConvMlp,
      mlp_ratio=4,
      act_layer=nn.GELU,
      ls_init_value=1e-6,
      drop_path=0.0,
  ):
      super().__init__()
      self.token_mixer = token_mixer(dim, dilation=dilation)
      self.norm = norm_layer(dim)
      self.mlp = mlp_layer(
          dim, int(mlp_ratio * dim), act_layer=act_layer
      )
      self.gamma = (
          nn.Parameter(ls_init_value * torch.ones(dim))
          if ls_init_value
          else None
      )
      self.drop_path = (
          DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
      )
  
imports: from functools import partial
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import trunc_normal_, DropPath, to_2tuple, get_padding, SelectAdaptivePool2d
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 1 to create a new MetaNeXtBlock object from inherited base classes: nn with example: obj = MetaNeXtBlock(dim, dilation, token_mixer, norm_layer, mlp_layer, mlp_ratio, act_layer, ls_init_value, drop_path)