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 empty_field(self):
      # Pass the padding_value, so that any outer field, e.g., `ListField[TensorField]` uses the
      # same padding_value in the padded ArrayFields
      return TensorField(
          torch.tensor([], dtype=self.tensor.dtype),
          padding_value=self.padding_value,
      )
  
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 4 for calling the TensorField obj's underlying member method code with example usage: obj.empty_field() and returns: TensorField