class_docstr: A class representing a tensor, which could have arbitrary dimensions.
  A batch of these tensors are padded to the max dimension length in the batch
  for each dimension.
class_docstr_tok: ["A","class","representing","a","tensor",",","which","could","have","arbitrary","dimensions",".","A","batch","of","these","tensors","are","padded","to","the","max","dimension","length","in","the","batch","for","each","dimension","."]
code: def __str__(self) -> str:
      return f"TensorField with shape: {self.tensor.size()} and dtype: {self.tensor.dtype}."
  
file_path: allennlp/allennlp/data/fields/tensor_field.py
imports: from typing import Dict, Any, Union, Optional
  import torch
  import numpy
  from allennlp.data.fields.field import Field
  from allennlp.common.util import JsonDict
label_desc: Use image node_id 5 for calling the TensorField obj's underlying member method code with example usage: obj.__str__() and returns: str, self, str, self, str