class_docstr: Tests for derivative of a sample ~ Gamma(alpha, beta) wrt alpha and beta.
  
  The sample is an "implicit" function of alpha, beta and the independent random
  noise u. The derivatives we are looking for are
  d sample(alpha, beta, u) / dalpha (and dbeta).
  
  The derivative w.r.t. beta is computed by the standard automatic
  differentiation, so we trust that it is computed correctly.
  
  The derivative w.r.t. alpha is computed by Eigen function, so we test it in
  several ways. Unfortunately, the standard derivative checking by perturbing
  the parameter is impossible here, because we cannot fix the value of u
  in the random sampler. Instead, we compare the derivative for the given pair
  of (sample, alpha) to the values computed in various ways, and also check
  some statistical properties of the derivative.
code: def testCompareToExplicitDerivativeFloat(self):
      self._testCompareToExplicitDerivative(dtypes.float32)
  
imports: import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_grad
  from tensorflow.python.ops import random_ops
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 5 for calling the RandomGammaGradTest obj's underlying member method code with example usage: obj.testCompareToExplicitDerivativeFloat() without return types