title: Is sympy solve compatible with tensorflow GradientTape?
body: <p>I want to solve p for Vo using sympy solve, an algorithm that solves an equation without an initial value. Then, I want to find the derivative of p for Vo using TensorFlow's automatic differentiation. I wrote the code below, and the value was calculated well. However, it could not be converted to tensorflow dtype at the end. Is there a way to use TensorFlow tensors compatible with sympy?</p>
  <pre><code>from sympy import solve, Symbol, re
  import tensorflow as tf`
  
  class Concentrate_calculator:
      def __init__(self, k, k1, m):
          self.k=k
          self.k1=k1
          self.m=m
      
      def calp(self, Vo):
          p=Symbol('p')
          equation = k1*m/(p+k1)+k/p-2*Vo-p
          solution = solve(equation, p)
          p=float(re(solution[2]))
          return p
  
  
  k=10.0**15
  k1=10.0**9.5
  m=10.0**19.2    
  
  cal = Concentrate_calculator(k,k1,m)
  
  Vo = tf.Variable(1e18)
  #Vo=1e18
  with tf.GradientTape() as t:
      t.watch(Vo)
      p=cal.calp(Vo)
    
  dpdVo = t.gradient(p,Vo)
  print(dpdVo)
  </code></pre>
  <p>TypeError: Cannot convert value 21897084140.095097 to a TensorFlow DType.</p>