class_docstr: Patch Merging Layer.
  Args:
      dim (int): Number of input channels.
      norm_layer (str): Normalization layer name.
      strided (Optional[bool]): Down-sample the input by a factor of 2. Default is True.
class_docstr_tok: ["Patch","Merging","Layer",".","Args",":","dim","(","int",")",":","Number","of","input","channels",".","norm_layer","(","str",")",":","Normalization","layer","name",".","strided","(","Optional","[","bool","]",")",":","Down-sample","the","input","by","a","factor","of","2",".","Default","is","True","."]
code: def __repr__(self) -> str:
      s = f"{self.__class__.__name__}(dim={self.dim})"
      return s
  
imports: from typing import List, Optional
  import torch
  from torch import Tensor, nn
  from torch.nn import functional
  from cvnets.layers import Dropout, LinearLayer, StochasticDepth, get_normalization_layer
  from cvnets.layers.activation import build_activation_layer
  from cvnets.modules import BaseModule
label_desc: Use image node_id 3 for calling the PatchMerging obj's underlying member method code with example usage: obj.__repr__() and returns: s