class_docstr: MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d
      
class_docstr_tok: ["MLP","w\/","Global","Response","Norm","(","see","grn.py",")",",","nn.Linear","or","1x1","Conv2d"]
code: def __init__(
      self,
      in_features,
      hidden_features=None,
      out_features=None,
      act_layer=nn.GELU,
      bias=True,
      drop=0.0,
      use_conv=False,
  ):
      super().__init__()
      out_features = out_features or in_features
      hidden_features = hidden_features or in_features
      bias = to_2tuple(bias)
      drop_probs = to_2tuple(drop)
      linear_layer = (
          partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
      )
  
      self.fc1 = linear_layer(
          in_features, hidden_features, bias=bias[0]
      )
      self.act = act_layer()
      self.drop1 = nn.Dropout(drop_probs[0])
      self.grn = GlobalResponseNorm(
          hidden_features, channels_last=not use_conv
      )
      self.fc2 = linear_layer(
          hidden_features, out_features, bias=bias[1]
      )
      self.drop2 = nn.Dropout(drop_probs[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 GlobalResponseNormMlp object from inherited base classes: nn with example: obj = GlobalResponseNormMlp(in_features, hidden_features, out_features, act_layer, bias, drop, use_conv)