class_docstr: MLP using 1x1 convs that keeps spatial dims
      
class_docstr_tok: ["MLP","using","1x1","convs","that","keeps","spatial","dims"]
code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.ReLU,
      norm_layer=None,
      bias=True,
      drop=0.0,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      bias = to_2tuple(bias)
  
      self.fc1 = nn.Conv2d(
          in_features, hidden_features, kernel_size=1, bias=bias[0]
      )
      self.norm = (
          norm_layer(hidden_features) if norm_layer else nn.Identity()
      )
      self.act = act_layer()
      self.drop = nn.Dropout(drop)
      self.fc2 = nn.Conv2d(
          hidden_features, out_features, kernel_size=1, bias=bias[1]
      )
  
imports: from functools import partial
  from torch import nn
  from .grn import GlobalResponseNorm
  from .helpers import to_2tuple
label_desc: Use image node_id 1 to create a new ConvMlp object from inherited base classes: nn with example: obj = ConvMlp(in_features, hidden_features, out_features, act_layer, norm_layer, bias, drop)