class_docstr: 1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
  
  Basically works like a linear layer but the weights are transposed.
  
  Args:
      nf (`int`): The number of output features.
      nx (`int`): The number of input features.
class_docstr_tok: ["1D-convolutional","layer","as","defined","by","Radford","et","al",".","for","OpenAI","GPT","(","and","also","used","in","GPT-2",")",".","Basically","works","like","a","linear","layer","but","the","weights","are","transposed",".","Args",":","nf","(","`","int","`",")",":","The","number","of","output","features",".","nx","(","`","int","`",")",":","The","number","of","input","features","."]
code: def __init__(self, nf, nx):
      super().__init__()
      self.nf = nf
      w = torch.empty(nx, nf)
      nn.init.normal_(w, std=0.02)
      self.weight = nn.Parameter(w)
      self.bias = nn.Parameter(torch.zeros(nf))
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pytorch_utils.py
imports: import inspect
  from typing import Callable, List, Optional, Set, Tuple, Union
  import torch
  from packaging import version
  from torch import _softmax_backward_data, nn
  from .utils import logging
label_desc: Use image node_id 1 to create a new Conv1D object from inherited base classes: nn with example: obj = Conv1D(nf, nx)