class_docstr: A LinearBn1d module is a module fused from Linear and BatchNorm1d, attached
  with FakeQuantize modules for weight, used in quantization aware training.
  
  We combined the interface of :class:`torch.nn.Linear` and
  :class:torch.nn.BatchNorm1d`.
  
  Similar to :class:`torch.nn.Linear`, with FakeQuantize modules initialized
  to default.
  
  Attributes:
      freeze_bn:
      weight_fake_quant: fake quant module for weight
class_docstr_tok: ["A","LinearBn1d","module","is","a","module","fused","from","Linear","and","BatchNorm1d",",","attached","with","FakeQuantize","modules","for","weight",",","used","in","quantization","aware","training",".","We","combined","the","interface","of",":","class",":","`","torch.nn.Linear","`","and",":","class",":","torch.nn.BatchNorm1d","`",".","Similar","to",":","class",":","`","torch.nn.Linear","`",",","with","FakeQuantize","modules","initialized","to","default",".","Attributes",":","freeze_bn",":","weight_fake_quant",":","fake","quant","module","for","weight"]
code: def freeze_bn_stats(self):
      self.freeze_bn = True
      self.bn.training = False
      return self
  
file_path: pytorch/torch/ao/nn/intrinsic/qat/modules/linear_fused.py
imports: import torch
  import torch.nn
  import torch.ao.nn.intrinsic
  import torch.nn.functional
  from torch.nn import init
  from torch.nn.parameter import Parameter
  from torch.nn.utils.fusion import fuse_linear_bn_weights
label_desc: Use image node_id 6 for calling the LinearBn1d obj's underlying member method code with example usage: obj.freeze_bn_stats() and returns: self