class_docstr: An embedding bag module attached with FakeQuantize modules for weight,
  used for quantization aware training.
  
  We adopt the same interface as `torch.nn.EmbeddingBag`, please see
  https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag
  for documentation.
  
  Similar to `torch.nn.EmbeddingBag`, with FakeQuantize modules initialized to
  default.
  
  Attributes:
      weight: fake quant module for weight
class_docstr_tok: ["An","embedding","bag","module","attached","with","FakeQuantize","modules","for","weight",",","used","for","quantization","aware","training",".","We","adopt","the","same","interface","as","`","torch.nn.EmbeddingBag","`",",","please","see","https",":","\/\/pytorch.org\/docs\/stable\/generated\/torch.nn.EmbeddingBag.html","#","torch.nn.EmbeddingBag","for","documentation",".","Similar","to","`","torch.nn.EmbeddingBag","`",",","with","FakeQuantize","modules","initialized","to","default",".","Attributes",":","weight",":","fake","quant","module","for","weight"]
code: def to_float(self):
      embedding_bag = torch.nn.EmbeddingBag(
          self.num_embeddings,
          self.embedding_dim,
          self.max_norm,
          self.norm_type,
          self.scale_grad_by_freq,
          self.mode,
          self.sparse,
          None,
          self.include_last_offset,
          self.padding_idx,
      )
      embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
      embedding_bag.train(self.training)
      return embedding_bag
  
file_path: pytorch/torch/ao/nn/qat/modules/embedding_ops.py
imports: import torch
  from torch import Tensor
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 4 for calling the EmbeddingBag obj's underlying member method code with example usage: obj.to_float() and returns: embedding_bag