import tensorflow as tf

  Returns:
    logits: the pre-softmax activations, a tensor of size
      [batch_size, num_classes]
    end_points: a dictionary from components of the nets to the corresponding
      activation.
  """
  assert data_format in ['NDHWC', 'NCDHW']
  # Final pooling and prediction
  with tf.variable_scope(
      scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
    with arg_scope(
        [layers.batch_norm, layers.dropout], is_training=is_training):
      net, end_points = s3dg_base(
          inputs,
          first_temporal_kernel_size=first_temporal_kernel_size,
          temporal_conv_startat=temporal_conv_startat,
