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.
code: def forward(self, x):
      size_out = x.size()[:-1] + (self.nf,)
      x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
      x = x.view(size_out)
      return x
  
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
label_desc: Use image node_id 2 for calling the Conv1D obj's underlying member method code with example usage: obj.forward(x) and returns: x