parameters: (self, input_data, data_spec)
return_statement: 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)
docstring: 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.
docstring_summary: Constructs a TF graph for evaluating a random tree.
docstring_tokens: Constructs a TF graph for evaluating a random tree .
function: def inference_graph(self, input_data, data_spec):
      """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.
      """
      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)
function_tokens: def inference_graph ( self , input_data , data_spec ) : 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 )