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 as_tensor(self, padding_lengths: Dict[str, int]) -> torch.Tensor:
      tensor = self.tensor
      while len(tensor.size()) < len(padding_lengths):
          tensor = tensor.unsqueeze(-1)
      pad = [
          padding
          for i, dimension_size in reversed(
              list(enumerate(tensor.size()))
          )
          for padding in [
              0,
              padding_lengths["dimension_" + str(i)] - dimension_size,
          ]
      ]
      return torch.nn.functional.pad(
          tensor, pad, 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 3 for calling the TensorField obj's underlying member method code with example usage: obj.as_tensor(padding_lengths) and returns: torch