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 __eq__(self, other) -> bool:
      if isinstance(self, other.__class__):
          return (
              torch.equal(self.tensor, other.tensor)
              and self.padding_value == other.padding_value
          )
      return NotImplemented
  
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 7 for calling the TensorField obj's underlying member method code with example usage: obj.__eq__(other) and returns: NotImplemented, unknown