class_docstr: A ConvReLU1d module is a fused module of Conv1d and ReLU
  
  We adopt the same interface as :class:`torch.ao.nn.quantized.Conv1d`.
  
  Attributes:
      Same as torch.ao.nn.quantized.Conv1d
class_docstr_tok: ["A","ConvReLU1d","module","is","a","fused","module","of","Conv1d","and","ReLU","We","adopt","the","same","interface","as",":","class",":","`","torch.ao.nn.quantized.Conv1d","`",".","Attributes",":","Same","as","torch.ao.nn.quantized.Conv1d"]
code: def from_float(cls, mod):
      if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU1d:
          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 ConvReLU1d obj's underlying member method code with example usage: obj.from_float(cls, mod) and returns: super