// XlaBuilder. This function adds HLO to a larger HLO computation, so
// HLO-level inputs are supplied, and HLO-level outputs are produced.
// xla_params is the HLO-level inputs and returns is the HLO-level outputs.
// If unconditionally_use_output_shapes is true then the unregistered
// attribute _output_shapes is always used to set the output shapes of the ops.
ABSL_DEPRECATED(
    "Use v1/compile_tf_graph.h::CompileTensorflowGraphToHlo instead.")

        then:
        groupedOutput.taskCount == 3
        groupedOutput.task(':1:log').output == 'Output from 1'
        groupedOutput.task(':2:log').output == 'Output from 2\nMore output from 2'
        groupedOutput.task(':3:log').output == 'Output from 3\n\n\n\nHandles lots of newline characters'
    }

    def "parses incremental tasks"() {
        given:
        def consoleOutput = """

    output_types.push_back(
        strings::StrCat("::tensorflow::", is_list ? "OutputList" : "Output"));
    output_names.push_back(AvoidCPPKeywords(api_def_arg.rename_to()));
    is_list_output.push_back(is_list);
  }

  strings::StrAppend(&comment, "\nReturns:\n");
  if (graph_op_def.output_arg_size() == 0) {  // No outputs.
    strings::StrAppend(&comment, "* the created `Operation`\n");

	// We can simulate a scaling arbitrary precision x * (n/2⁶⁴) by
	// the high bits of a double-width multiply of x*n, meaning (x*n)/2⁶⁴.
	// Since there are 2⁶⁴ possible inputs x and only n possible outputs,
	// the output is necessarily biased if n does not divide 2⁶⁴.
	// In general (x*n)/2⁶⁴ = k for x*n in [k*2⁶⁴,(k+1)*2⁶⁴).
	// There are either floor(2⁶⁴/n) or ceil(2⁶⁴/n) possible products
	// in that range, depending on k.

          IRMapping mapping;
          indexed_regions.value().cloneInto(&target_region, mapping);
        }
      }
      for (auto output : outputs_replaced) {
        output.getFirst().replaceAllUsesWith(
            quantized_op->getResult(output.getSecond()));
      }
    }
    return success();
  }
};

// Populates common patterns that are usually compute heavy or memory bound.

            validateSynonymString(action, newInputs, "inputs", hook);
            entity.setNewInputs(newInputs);
            final String[] newOutputs = splitLine(form.outputs);
            validateSynonymString(action, newOutputs, "outputs", hook);
            entity.setNewOutputs(newOutputs);
            return entity;
        });
    }

          IRMapping mapping;
          indexed_regions.value().cloneInto(&target_region, mapping);
        }
      }
      for (auto output : outputs_replaced) {
        output.getFirst().replaceAllUsesWith(
            quantized_op->getResult(output.getSecond()));
      }

      // To verify the numericals, the original floating-point ops are

        if (subgraph->inputs()->Get(i) == tensor_idx) {
          return true;
        }
      }
      for (size_t i = 0; i < subgraph->outputs()->size(); ++i) {
        if (subgraph->outputs()->Get(i) == tensor_idx) {
          return true;
        }
      }
    }
    return false;
  }

  // Returns the producer op code of the specified tensor_idx.

				return configWriter.PrintClusterDump(filter, outputFormat)
			default:
				return fmt.Errorf("output format %q not supported", outputFormat)
			}
		},
		ValidArgsFunction: completion.ValidPodsNameArgs(ctx),
	}

	clusterConfigCmd.PersistentFlags().StringVarP(&outputFormat, "output", "o", summaryOutput, "Output format: one of json|yaml|short")

  // the list of non-static tensors that feed into the subgraph for inference.
  inputs:[int];

  // Indices of the tensors that are outputs out of this subgraph. Note this is
  // the list of output tensors that are considered the product of the
  // subgraph's inference.
  outputs:[int];

  // All operators, in execution order.
  operators:[Operator];

  // Name of this subgraph (used for debugging).