class_docstr: Positional embedding which is placed at the beginning of Transformer.
  
  Args:
      embed_dim (int): Feature dimension of the input Tensor.
      kernel_size (int): The number of frames to be use.
      groups (int): The number of groups in feature dimensions.
class_docstr_tok: ["Positional","embedding","which","is","placed","at","the","beginning","of","Transformer",".","Args",":","embed_dim","(","int",")",":","Feature","dimension","of","the","input","Tensor",".","kernel_size","(","int",")",":","The","number","of","frames","to","be","use",".","groups","(","int",")",":","The","number","of","groups","in","feature","dimensions","."]
code: def __prepare_scriptable__(self):
      if self.conv.__class__.__name__ == "ParametrizedConv1d":
          _LG.warning(
              "Removing weight_norm from %s", self.__class__.__name__
          )
          torch.nn.utils.parametrize.remove_parametrizations(
              self.conv, "weight"
          )
      return self
  
imports: import logging
  from typing import List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import Module, Parameter
  from .wavlm_attention import WavLMSelfAttention
label_desc: Use image node_id 2 for calling the ConvolutionalPositionalEmbedding obj's underlying member method code with example usage: obj.__prepare_scriptable__() and returns: self