def get_synth_data(height, width, num_channels, num_classes, dtype):
    'Creates a set of synthetic random data.\n\n  Args:\n    height: Integer height that will be used to create a fake image tensor.\n    width: Integer width that will be used to create a fake image tensor.\n    num_channels: Integer depth that will be used to create a fake image tensor.\n    num_classes: Number of classes that should be represented in the fake labels\n      tensor\n    dtype: Data type for features/images.\n\n  Returns:\n    A tuple of tensors representing the inputs and labels.\n\n  '
    inputs = tf.random.truncated_normal([height, width, num_channels], dtype=dtype, mean=127, stddev=60, name='synthetic_inputs')
    labels = tf.random.uniform([1], minval=0, maxval=(num_classes - 1), dtype=tf.int32, name='synthetic_labels')
    return (inputs, labels)
Creates a set of synthetic random data.

Args:
  height: Integer height that will be used to create a fake image tensor.
  width: Integer width that will be used to create a fake image tensor.
  num_channels: Integer depth that will be used to create a fake image tensor.
  num_classes: Number of classes that should be represented in the fake labels
    tensor
  dtype: Data type for features/images.

Returns:
  A tuple of tensors representing the inputs and labels.
def get_synth_data(height, width, num_channels, num_classes, dtype):
    'Creates a set of synthetic random data.\n\n  Args:\n    height: Integer height that will be used to create a fake image tensor.\n    width: Integer width that will be used to create a fake image tensor.\n    num_channels: Integer depth that will be used to create a fake image tensor.\n    num_classes: Number of classes that should be represented in the fake labels\n      tensor\n    dtype: Data type for features/images.\n\n  Returns:\n    A tuple of tensors representing the inputs and labels.\n\n  '
    inputs = tf.random.truncated_normal([height, width, num_channels], dtype=dtype, mean=127, stddev=60, name='synthetic_inputs')
    labels = tf.random.uniform([1], minval=0, maxval=(num_classes - 1), dtype=tf.int32, name='synthetic_labels')
    return (inputs, labels)