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)
      self.weight = torch.nn.Parameter(w)
      self.bias = torch.nn.Parameter(torch.zeros(nf))
  
imports: import torch
  import pytest
  import random
  import numpy
  from unit.megatron_model import get_gpt2_model
  from deepspeed.compression.compress import init_compression
  from unit.modeling import BertConfig
  from unit.modelingpreln import BertEncoder
  from deepspeed.compression.basic_layer import LinearLayer_Compress, ColumnParallelLinear_Compress, RowParallelLinear_Compress
  from deepspeed.compression.helper import convert_conv1d_to_linear
  from deepspeed.accelerator import get_accelerator
  from deepspeed.runtime.utils import required_torch_version
  from unit.common import DistributedTest
label_desc: Use image node_id 1 to create a new Conv1D object from inherited base classes: torch.nn with example: obj = Conv1D(nf, nx)