class_docstr: A ConvReLU2d module is a fused module of Conv2d and ReLU
  
  We adopt the same interface as :class:`torch.ao.nn.quantized.Conv2d`.
  
  Attributes:
      Same as torch.ao.nn.quantized.Conv2d
class_docstr_tok: ["A","ConvReLU2d","module","is","a","fused","module","of","Conv2d","and","ReLU","We","adopt","the","same","interface","as",":","class",":","`","torch.ao.nn.quantized.Conv2d","`",".","Attributes",":","Same","as","torch.ao.nn.quantized.Conv2d"]
code: def forward(self, input):
      # Temporarily using len(shape) instead of ndim due to JIT issue
      # https://github.com/pytorch/pytorch/issues/23890
      if len(input.shape) != 4:
          raise ValueError("Input shape must be `(N, C, H, W)`!")
      if self.padding_mode != "zeros":
          _reversed_padding_repeated_twice = _reverse_repeat_padding(
              self.padding
          )
          input = F.pad(
              input,
              _reversed_padding_repeated_twice,
              mode=self.padding_mode,
          )
      return torch.ops.quantized.conv2d_relu(
          input, self._packed_params, self.scale, self.zero_point
      )
  
imports: import torch
  import torch.ao.nn.intrinsic
  import torch.ao.nn.intrinsic.qat
  import torch.nn.functional
  import torch.ao.nn.quantized
  from torch.nn.utils import fuse_conv_bn_weights
label_desc: Use image node_id 2 for calling the ConvReLU2d obj's underlying member method code with example usage: obj.forward(input) and returns: torch