class_docstr: 1x1 convolution.
class_docstr_tok: ["1x1","convolution","."]
code: def __init__(self, in_ch, out_ch, stride=1, kernel=1, fm_size=7):
      super(Conv1x1, self).__init__()
      assert stride in [1, 2], "stride should be in [1, 2]"
      padding = kernel // 2
  
      self.conv = nn.Sequential(
          nn.Conv2d(in_ch, out_ch, kernel, stride, padding),
          nn.ReLU(inplace=True),
      )
  
imports: from __future__ import absolute_import, division, print_function
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Conv1x1 object from inherited base classes: nn with example: obj = Conv1x1(in_ch, out_ch, stride, kernel, fm_size)