def inference_graph(self, input_data, data_spec):
    'Constructs a TF graph for evaluating a random tree.\n\n    Args:\n      input_data: A tensor or SparseTensor or placeholder for input data.\n      data_spec: A list of tf.dtype values specifying the original types of\n        each column.\n\n    Returns:\n      The last op in the random tree inference graph.\n    '
    sparse_indices = []
    sparse_values = []
    sparse_shape = []
    if isinstance(input_data, ops.SparseTensor):
        sparse_indices = input_data.indices
        sparse_values = input_data.values
        sparse_shape = input_data.shape
        input_data = []
    return self.inference_ops.tree_predictions(input_data, sparse_indices, sparse_values, sparse_shape, data_spec, self.variables.tree, self.variables.tree_thresholds, self.variables.node_sums, valid_leaf_threshold=self.params.valid_leaf_threshold)
Constructs a TF graph for evaluating a random tree.

Args:
  input_data: A tensor or SparseTensor or placeholder for input data.
  data_spec: A list of tf.dtype values specifying the original types of
    each column.

Returns:
  The last op in the random tree inference graph.
def inference_graph(self, input_data, data_spec):
    'Constructs a TF graph for evaluating a random tree.\n\n    Args:\n      input_data: A tensor or SparseTensor or placeholder for input data.\n      data_spec: A list of tf.dtype values specifying the original types of\n        each column.\n\n    Returns:\n      The last op in the random tree inference graph.\n    '
    sparse_indices = []
    sparse_values = []
    sparse_shape = []
    if isinstance(input_data, ops.SparseTensor):
        sparse_indices = input_data.indices
        sparse_values = input_data.values
        sparse_shape = input_data.shape
        input_data = []
    return self.inference_ops.tree_predictions(input_data, sparse_indices, sparse_values, sparse_shape, data_spec, self.variables.tree, self.variables.tree_thresholds, self.variables.node_sums, valid_leaf_threshold=self.params.valid_leaf_threshold)