class_docstr: A ConvReLU3d module is a fused module of Conv3d and ReLU
  
  We adopt the same interface as :class:`torch.ao.nn.quantized.Conv3d`.
  
  Attributes: Same as torch.ao.nn.quantized.Conv3d
class_docstr_tok: ["A","ConvReLU3d","module","is","a","fused","module","of","Conv3d","and","ReLU","We","adopt","the","same","interface","as",":","class",":","`","torch.ao.nn.quantized.Conv3d","`",".","Attributes",":","Same","as","torch.ao.nn.quantized.Conv3d"]
code: def from_float(cls, mod):
      if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU3d:
          assert (
              mod.bn.running_var is not None
              and mod.bn.running_mean is not None
          )
          mod.weight, mod.bias = fuse_conv_bn_weights(
              mod.weight,
              mod.bias,
              mod.bn.running_mean,
              mod.bn.running_var,
              mod.bn.eps,
              mod.bn.weight,
              mod.bn.bias,
          )
      return super().from_float(mod)
  
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 4 for calling the ConvReLU3d obj's underlying member method code with example usage: obj.from_float(cls, mod) and returns: super