subject:
      - foo Object
    inputs:
      - other.thing String (java.lang.String) [*]

  by-type
    subject:
      - <no path> Runnable [*]
    inputs:
      - <no path> String (java.lang.String) [*]

  creator
    inputs:
      - a.b Object (a.b) [*]
'''
    }

    def "closes elements as required to bind all subjects and inputs"() {
        given:

    full_output_types.push_back(output.getType());
  }

  // Convert split sharded inputs to MANUAL sharded inputs.
  // common_split_sharding is the split sharding that is common to all inputs
  // and outputs.
  llvm::SmallVector<Value, 4> manual_inputs;
  manual_inputs.reserve(inputs.size());
  for (Value in : inputs) {
    Type shard_type;
    if (failed(GetShardShapedType(original_op, num_cores_per_replica,

It is possible to temporarily opt out of configuration input detection in the following cases:

* Since Gradle 8.1, using many APIs related to the file system is correctly tracked as configuration inputs, including the file system checks, such as `File.exists()` or `File.isFile()`.
+

    p.device = &dummy_device;
    p.step_id = 43;

    Tensor t(tensorflow::uint8(123));

    gtl::InlinedVector<TensorValue, 4> inputs;
    // Simulate 2 inputs
    inputs.emplace_back(&t);
    inputs.emplace_back();
    p.inputs = inputs;

    Status status;
    std::unique_ptr<OpKernel> kernel =
        GetFakeKernel(device_name, op_name, node_name, &status);
    TF_EXPECT_OK(status);

        }
        if (TF::TPUReplicatedInputOp input =
                llvm::dyn_cast<TF::TPUReplicatedInputOp>(user)) {
          if (!input.getIsPacked()) {
            input.emitOpError() << "unexpected variable input, not packed";
            return LogicalResult::failure();
          }

          if (is_variable) {
            input.emitOpError() << "unexpected multiple TPUReplicatedInputOp "

      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, grad_inputs, s_, grad_outputs);
    } else {
      TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
                                ninputs, nullptr, s_, grad_outputs);
    }
  }

  void BuildErrorGraph(TF_Output* inputs, TF_Output* outputs) {

  } else if (sparse_index_vector.type ==
             tflite::SparseIndexVector_Uint16Vector) {
    const auto& inputs = sparse_index_vector.AsUint16Vector()->values;
    std::vector<int32_t> outputs(inputs.size());
    std::transform(inputs.begin(), inputs.end(), outputs.begin(),
                   [](auto x) { return static_cast<int32_t>(x); });
    return outputs;
  } else if (sparse_index_vector.type ==

  let summary = [{
output = input; While (Cond(output)) { output = Body(output) }
  }];

  let description = [{
output = input; While (Cond(output)) { output = Body(output) }

input: A list of input tensors whose types are T.
output: A list of output tensors whose types are T.
cond: A function that takes 'input' and returns a tensor.  If the tensor is

// Predicate denote logical formulas and mapping a node `n` to a predicate
// `pred` implies that `n` is live whenever `pred` is true.  Then we can deduce
// mismatching liveness in the inputs to node by comparing the predicate those
// inputs are mapped to.  The core logic of this pass resides in creating the
// map from TensorFlow nodes to predicates.
//
//
// MAPPING NODES TO PREDICATES, MODULO CYCLES

Updates inputs to TPU embedding enqueue ops depending on whether graph
is in training mode or in evaluation mode.
### `-tf-tpu-validate-inputs`

_Validates inputs to the TPU TF/XLA bridge_

This pass checks that the IR has valid input to TPU TF/XLA bridge.
It checks the relations of multiple ops. Properties of single ops are
checked by the 'verify' method of ops.