class_docstr: Tests for the numeric computation of VJPs
class_docstr_tok: ["Tests","for","the","numeric","computation","of","VJPs"]
docstr: Test that using the TensorFlow interface the dtype of the result is
  determined by the dtype of the dy.
docstr_tok: ["Test","that","using","the","TensorFlow","interface","the","dtype","of","the","result","is","determined","by","the","dtype","of","the","dy","."]
code: def test_dtype_tf(self, dtype1, dtype2):
      """Test that using the TensorFlow interface the dtype of the result is
      determined by the dtype of the dy."""
      import tensorflow as tf
  
      dtype = dtype1
      dtype1 = getattr(tf, dtype1)
      dtype2 = getattr(tf, dtype2)
  
      a = tf.ones((1), dtype=dtype1)
      b = tf.ones((2), dtype=dtype1)
  
      dy = tuple([a, b])
      jac = tuple(
          [tf.ones((1), dtype=dtype2), tf.ones((2), dtype=dtype2)]
      )
  
      assert qml.gradients.compute_vjp_multi(dy, jac)[0].dtype == dtype
  
imports: from functools import partial
  import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.gradients import param_shift
label_desc: Use image node_id 13 for calling the TestComputeVJP obj's underlying member method code with example usage: obj.test_dtype_tf(dtype1, dtype2) without return types