return errors::InvalidArgument(
        "Must specify a gradient input for each output.");
  }
  std::vector<bool> reachable_nodes = GetReachableNodes();
  for (const Output& input : inputs_) {
    if (!reachable_nodes[input.node()->id()]) {
      return errors::InvalidArgument(
          "Cannot compute the partial derivative for node '",
          input.node()->name(),
          "' as it's unreachable from the output node(s).");

  Init(2);
  // Create nested loop:
  //  while (input1 < 6) {
  //    inner_input1 = input1
  //    while (inner_input1 < 3) {
  //      input2 += 1
  //      inner_input1 += 2
  //    }
  //    input1 += input2
  //  }
  //
  // Expected execution with initial values input1 = input2 = 0:
  //
  // outer inner               inner_
  // step# step# input1 input2 input1
  // ------------------------------------

  }
  func.return
}

// CHECK:      [[INPUT_A:%.+]], {{%.+}} = tf_executor.island {
// CHECK-NEXT:   [[OP_A_OUTPUT:%.+]] = "tf.opA"([[ARG_0]])
// CHECK-NEXT:   tf_executor.yield [[OP_A_OUTPUT]] :
// CHECK:      [[ISLAND_0_OUTPUT:%.+]], {{%.+}} = tf_executor.island {
// CHECK-NEXT:   [[OP_B_OUTPUT:%.+]] = "tf.opB"([[INPUT_A]]) {_parallel_execution_ids = "p0:0"}
// CHECK:        tf_executor.yield [[OP_B_OUTPUT]] :

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input -tf-input-shapes='' -tf-output-arrays=input -tf-convert-legacy-fed-inputs -o - | FileCheck --check-prefix=NODATATYPE %s

```python
import tensorflow as tf

@Composite(
    'FusedFullyConnected',
    inputs=['input_: T', 'filter_: T', 'bias: T'],
    attrs=['act: {"", "RELU", "RELU6", "TANH"} = ""'],
    derived_attrs=['T: {float, int8}'],
    outputs=['o: T'])
def _composite_fully_connected(input_, filter_, bias, act):
  res = tf.raw_ops.MatMul(
      a=input_, b=filter_, transpose_a=False, transpose_b=True)
  res = tf.raw_ops.Add(x=res, y=bias)

               op_name,
               inputs=None,
               attrs=None,
               derived_attrs=None,
               outputs=None):
    self._op_name = op_name
    self._inputs = inputs
    self._attrs = attrs
    self._derived_attrs = derived_attrs
    self._outputs = outputs

  def __call__(self, compose_fn):
    # TODO(fengliuai): more sanity check of the input function and make sure

  %data_format = tfr.constant "NHWC" -> !tfr.attr
  %MaxPool = tfr.call @tf__max_pool(%input_, %stride, %filter, %padding, %explicit_paddings, %data_format) : (!tfr.tensor, !tfr.attr, !tfr.attr, !tfr.attr, !tfr.attr, !tfr.attr) -> (!tfr.tensor)
  tfr.return %MaxPool : !tfr.tensor
// CHECK: tf__max_pool
}

// CHECK-LABEL: @tf__cast_float
tfr.func @tf__cast_float(%input_: !tfr.tensor, %out_type: !tfr.attr{tfr.name="out_type"}) -> (!tfr.tensor) {

          .format(op_name, expected_args, all_func_args))

    cxx_reg_code = ['\nREGISTER_OP("{}")'.format(op_name)]
    for input_ in inputs:
      cxx_reg_code.append('.Input("{}")'.format(input_))
    for attr in attrs:
      py_str = attr.replace('"', "'")
      cxx_reg_code.append('.Attr("{}")'.format(py_str))
    for attr in all_dec_args.get('derived_attrs', []):

Robert Griesemer <******@****.***> 1408667113 -0700

Robert Griesemer <******@****.***> 1408667113 -0700