docstr: Compute inference function from MNIST beginners tutorial.
docstr_tok: ["Compute","inference","function","from","MNIST","beginners","tutorial","."]
code: def testMnistForwardFunc(self):
      """Compute inference function from MNIST beginners tutorial."""
      batch_size = 16
      image_size = 28 * 28
      num_classes = 10
  
      # Define a TensorFlow function to compute the forward pass.
      def MnistForward(w, b, x):
          return nn_ops.softmax(math_ops.matmul(x, w) + b)
  
      w = np.random.random_sample((image_size, num_classes)).astype(
          np.float32
      )
      b = np.random.random_sample((num_classes)).astype(np.float32)
      x = np.random.random_sample((batch_size, image_size)).astype(
          np.float32
      )
      self._compare(MnistForward, [w, b, x])
  
imports: import os
  import numpy
  from tensorflow.compiler.tests import test_utils
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.core.protobuf import rewriter_config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.compiler.xla import jit
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import function
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.python.platform import test
label_desc: Use image node_id 9 for calling the JitLaunchTest obj's underlying member method code with example usage: obj.testMnistForwardFunc() and returns: nn_ops