Status ReluModel(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> inputs,
                 absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Relu(ctx, inputs[0], &outputs[0], "Relu");
}

Status SparseSoftmaxCrossEntropyWithLogitsModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  AbstractTensorHandle* loss;

                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params) {
  tracing::TracingTensorHandle* handle = nullptr;
  for (auto input : inputs) {
    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);

}
Status TapeOperation::AddInput(AbstractTensorHandle* input) {
  TF_RETURN_IF_ERROR(parent_op_->AddInput(input));
  forward_op_.inputs.push_back(input);
  return absl::OkStatus();
}
Status TapeOperation::AddInputList(
    absl::Span<AbstractTensorHandle* const> inputs) {
  TF_RETURN_IF_ERROR(parent_op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_.inputs.push_back(input);
  }
  return absl::OkStatus();
}

Anis Eleuch <******@****.***> 1712232280 +0100

        def actualDir = file("actualDir").createDir()
        file("symlink1").createLink(actualDir)
        file("symlink2").createLink(actualDir)

        buildFile << """
            task myTask {
                def outputFile = file("build/output.txt")
                inputs.dir("symlink1")
                inputs.dir("symlink2")

#include "tensorflow/core/graph/graph.h"

namespace tensorflow {

// This analyzes a TensorFlow graph to identify nodes which may have partially
// dead inputs (i.e. these nodes may have some dead inputs and some alive
// inputs).
//
// For example, the ADD node in the following graph
//
//      V0  PRED0    V1  PRED1
//       |    |       |    |
//       v    v       v    v
//       SWITCH       SWITCH

                         absl::Span<AbstractTensorHandle* const> inputs,
                         int input_index, bool use_function,
                         AbstractTensorHandle** numerical_grad) {
  vector<AbstractTensorHandle*> theta_inputs(inputs.size());
  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =

  SmallVector<StringRef> inputs;
  mlir::dyn_cast_or_null<StringAttr>(entry_function_attr.get("inputs"))
      .strref()
      .split(inputs, /*Separator=*/",");

  return inputs;
}

void ConvertMainArgAttrs(func::FuncOp main_func_op, const int arg_idx,
                         const StringRef index_path) {
  const ArrayRef<NamedAttribute> arg_attrs =

                    Assert.returnTypesFor task.inputs, "dir", TaskInputFilePropertyBuilder, TaskInputFilePropertyBuilderInternal
                    Assert.returnTypesFor task.inputs, "file", TaskInputFilePropertyBuilder, TaskInputFilePropertyBuilderInternal
                    Assert.returnTypesFor task.inputs, "files", TaskInputFilePropertyBuilder, TaskInputFilePropertyBuilderInternal
                }

OpView::OpView(OpSpec op)
    : op_(op),
      input_args_(op_.Inputs().begin(), op_.Inputs().end()),
      output_args_(op_.Outputs().begin(), op_.Outputs().end()),
      argument_attrs_(op_.Attributes().begin(), op_.Attributes().end()) {
  // Initialize function arguments
  all_arguments_.push_back(OpArgumentView("AbstractContext*", "ctx"));
  for (const auto& arg : op_.Inputs()) {
    all_arguments_.push_back(OpArgumentView(arg));
  }