code: def test_alternative_dtypes(self):
      shape = [3, 4, 5, 6]
      array = numpy.zeros(shape)
  
      # Setting dtype to numpy.int64 should produce a torch.LongTensor when field is converted to
      # a tensor
      array_field1 = TensorField(array, dtype=numpy.int64)
      returned_tensor1 = array_field1.as_tensor(
          array_field1.get_padding_lengths()
      )
      assert returned_tensor1.dtype == torch.int64
  
      # Setting dtype to numpy.uint8 should produce a torch.ByteTensor when field is converted to
      # a tensor
      array_field2 = TensorField(array, dtype=numpy.uint8)
      returned_tensor2 = array_field2.as_tensor(
          array_field2.get_padding_lengths()
      )
      assert returned_tensor2.dtype == torch.uint8
  
      # Padding should not affect dtype
      padding_lengths = {
          "dimension_" + str(i): 10 for i, _ in enumerate(shape)
      }
      padded_tensor = array_field2.as_tensor(padding_lengths)
      assert padded_tensor.dtype == torch.uint8
  
      # Empty fields should have the same dtype
      empty_field = array_field2.empty_field()
      assert empty_field.tensor.dtype == array_field2.tensor.dtype
  
file_path: allennlp/tests/data/fields/tensor_field_test.py
imports: import numpy
  import torch
  from allennlp.common.testing.test_case import AllenNlpTestCase
  from allennlp.data.fields import TensorField, ListField
label_desc: Use image node_id 8 for calling the TestTensorField obj's underlying member method code with example usage: obj.test_alternative_dtypes() without return types