code: def testStudentPDFAndLogPDF(self):
      batch_size = 6
      df = constant_op.constant([3.0] * batch_size)
      mu = constant_op.constant([7.0] * batch_size)
      sigma = constant_op.constant([8.0] * batch_size)
      df_v = 3.0
      mu_v = 7.0
      sigma_v = 8.0
      t = np.array([-2.5, 2.5, 8.0, 0.0, -1.0, 2.0], dtype=np.float32)
      student = student_t.StudentT(
          df, loc=mu, scale=-sigma
      )  # pylint: disable=invalid-unary-operand-type
  
      log_pdf = student.log_prob(t)
      self.assertEqual(log_pdf.get_shape(), (6,))
      log_pdf_values = self.evaluate(log_pdf)
      pdf = student.prob(t)
      self.assertEqual(pdf.get_shape(), (6,))
      pdf_values = self.evaluate(pdf)
  
      if not stats:
          return
  
      expected_log_pdf = stats.t.logpdf(
          t, df_v, loc=mu_v, scale=sigma_v
      )
      expected_pdf = stats.t.pdf(t, df_v, loc=mu_v, scale=sigma_v)
      self.assertAllClose(expected_log_pdf, log_pdf_values)
      self.assertAllClose(np.log(expected_pdf), log_pdf_values)
      self.assertAllClose(expected_pdf, pdf_values)
      self.assertAllClose(np.exp(expected_log_pdf), pdf_values)
  
imports: import importlib
  import math
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import random_seed
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops.distributions import student_t
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
label_desc: Use image node_id 1 for calling the StudentTTest obj's underlying member method code with example usage: obj.testStudentPDFAndLogPDF() without return types