class_docstr: 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,
      drop=0.0,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      self.fc1 = nn.Conv2d(
          in_features, hidden_features, kernel_size=1, bias=True
      )
      self.norm = (
          norm_layer(hidden_features) if norm_layer else nn.Identity()
      )
      self.act = act_layer()
      self.fc2 = nn.Conv2d(
          hidden_features, out_features, kernel_size=1, bias=True
      )
      self.drop = nn.Dropout(drop)
  
imports: import math
  from functools import partial
  from collections import namedtuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.modules.utils import _pair
  import hydra
  from einops import reduce, rearrange
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, drop)