class_docstr: Float inference for mnist model.
code: def __init__(self):
      self.weights = {
          "f1": tf.Variable(
              tf.random.truncated_normal(
                  [5, 5, num_channels, n_hidden_1],
                  stddev=0.1,
                  seed=seed,
              )
          ),
          "f2": tf.Variable(
              tf.random.truncated_normal(
                  [5, 5, n_hidden_1, n_hidden_2], stddev=0.1, seed=seed
              )
          ),
          "f3": tf.Variable(
              tf.random.truncated_normal(
                  [n_hidden_3, flatten_size], stddev=0.1, seed=seed
              )
          ),
          "f4": tf.Variable(
              tf.random.truncated_normal(
                  [num_classes, n_hidden_3], stddev=0.1, seed=seed
              )
          ),
      }
  
      self.biases = {
          "b1": tf.Variable(tf.zeros([n_hidden_1])),
          "b2": tf.Variable(tf.zeros([n_hidden_2])),
          "b3": tf.Variable(tf.zeros([n_hidden_3])),
          "b4": tf.Variable(tf.zeros([num_classes])),
      }
  
file_path: tensorflow/tensorflow/compiler/mlir/tfr/examples/mnist/mnist_train.py
imports: import os
  from absl import flags
  import tensorflow
  import tensorflow_datasets
  from tensorflow.compiler.mlir.tfr.examples.mnist import gen_mnist_ops
  from tensorflow.compiler.mlir.tfr.examples.mnist import ops_defs
  from tensorflow.python.framework import load_library
label_desc: Use image node_id 1 to create a new FloatModel object from inherited base classes: tf with example: obj = FloatModel()