class_docstr: A list of tensors of different sizes, backed by a (offset, size, data)
  tuple.
  
  Indexing by LongTensor returns a new TensorList with the selected list
  elements (similar to indexing a torch index_select_).
  
  Indexing by an int returns a torch.Tensor with that list element.
class_docstr_tok: ["A","list","of","tensors","of","different","sizes",",","backed","by","a","(","offset",",","size",",","data",")","tuple",".","Indexing","by","LongTensor","returns","a","new","TensorList","with","the","selected","list","elements","(","similar","to","indexing","a","torch","index_select_",")",".","Indexing","by","an","int","returns","a","torch.Tensor","with","that","list","element","."]
code: def unsqueeze(self, dim):
      return self.apply(lambda x: x.unsqueeze(dim))
  
imports: import torch
label_desc: Use image node_id 17 for calling the TensorList obj's underlying member method code with example usage: obj.unsqueeze(dim) and returns: self