class_docstr: A `TransformerTextField` is a collection of several tensors that are are a representation of text,
  tokenized and ready to become input to a transformer.
  
  The naming pattern of the tensors follows the pattern that's produced by the huggingface tokenizers,
  and expected by the huggingface transformers.
class_docstr_tok: ["A","`","TransformerTextField","`","is","a","collection","of","several","tensors","that","are","are","a","representation","of","text",",","tokenized","and","ready","to","become","input","to","a","transformer",".","The","naming","pattern","of","the","tensors","follows","the","pattern","that","'s","produced","by","the","huggingface","tokenizers",",","and","expected","by","the","huggingface","transformers","."]
code: def as_tensor(
      self, padding_lengths: Dict[str, int]
  ) -> Dict[str, torch.Tensor]:
      result = {}
      for name, padding_length in padding_lengths.items():
          tensor = getattr(self, name)
          if len(tensor.shape) > 1:
              tensor = tensor.squeeze(0)
          result[name] = torch.nn.functional.pad(
              tensor,
              (0, padding_length - tensor.shape[-1]),
              value=self.padding_token_id if name == "input_ids" else 0,
          )
      if "attention_mask" not in result:
          result["attention_mask"] = torch.tensor(
              [True] * self.input_ids.shape[-1]
              + [False]
              * (
                  padding_lengths["input_ids"]
                  - self.input_ids.shape[-1]
              ),
              dtype=torch.bool,
          )
      return result
  
file_path: allennlp/allennlp/data/fields/transformer_text_field.py
imports: from typing import Dict, Optional, List, Any, Union
  import torch
  import torch.nn.functional
  from allennlp.data.fields.field import Field
  from allennlp.nn import util
label_desc: Use image node_id 3 for calling the TransformerTextField obj's underlying member method code with example usage: obj.as_tensor(padding_lengths) and returns: result