def inference(images):
  """Build the CIFAR model up to where it may be used for inference.
  Args:

  Returns:
    logits: Output tensor with the   computed logits.
  """

  # Reshape input picture
  print('In Inference ', images.get_shape(), type(images))

  images = tf.reshape(images, shape=[-1, 32, 32, 3])

  _dropout = tf.Variable(dropout)  # dropout (keep probability)

  # Store layers weight & bias
  _weights = {
    'wc1': tf.Variable(tf.random_normal([5, 5, 3, out_conv_1], stddev=1e-3)),  # 5x5 conv, 3 input, 64 outputs
    'wc2': tf.Variable(tf.random_normal([5, 5, out_conv_1, out_conv_2], stddev=1e-3)),
  # 5x5 conv, 64 inputs, 64 outputs
    'wd1': tf.Variable(tf.random_normal([out_conv_2 * 8 * 8, n_hidden_1], stddev=1e-3)),
    'wd2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=1e-3)),
    'out': tf.Variable(tf.random_normal([n_hidden_2, NUM_CLASSES], stddev=1e-3))
  }

  _biases = {
    'bc1': tf.Variable(tf.random_normal([out_conv_1])),
    'bc2': tf.Variable(tf.random_normal([out_conv_2])),
    'bd1': tf.Variable(tf.random_normal([n_hidden_1])),
    'bd2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([NUM_CLASSES]))
  }

  # Convolution Layer 1
  with tf.name_scope('Conv1'):
    conv1 = conv2d(images, _weights['wc1'], _biases['bc1'])
    # Max Pooling (down-sampling)
    conv1 = max_pool(conv1, k=2)
    # norm1
    conv1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=9.0 / 0.001, beta=0.75,
                      name='norm1')
    # Apply Dropout
    conv1 = tf.nn.dropout(conv1, _dropout)

  # Convolution Layer 2
  with tf.name_scope('Conv2'):
    conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])
    # norm2
    conv2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,
                      name='norm2')
    # # Max Pooling (down-sampling)
    conv2 = max_pool(conv2, k=2)
    # Apply Dropout
    conv2 = tf.nn.dropout(conv2, _dropout)

  # Fully connected layer 1
  with tf.name_scope('Dense1'):
    dense1 = tf.reshape(conv2,
                        [-1, _weights['wd1'].get_shape().as_list()[0]])  # Reshape conv2 output to fit dense layer input
    dense1 = tf.nn.relu_layer(dense1, _weights['wd1'], _biases['bd1'])  # Relu activation
    dense1 = tf.nn.dropout(dense1, _dropout)  # Apply Dropout

  # Fully connected layer 2
  with tf.name_scope('Dense2'):
    dense2 = tf.nn.relu_layer(dense1, _weights['wd2'], _biases['bd2'])  # Relu activation

  # Output, class prediction
  logits = tf.add(tf.matmul(dense2, _weights['out']), _biases['out'])

  return logits
dataset/ETHPy150Open HamedMP/ImageFlow/example_project/my_cifar.py/inference/swapped operands of `/`