<div class="badge-wrapper">
    <a class="badge" href="https://dpeuniversity.gradle.com/app/courses/012de84f-fcd3-45d4-9c4c-284382eb3f3f/" target="_blank">
        <span class="badge-type button--blue">LEARN</span>
        <span class="badge-text">Introduction to Gradle for Developers&nbsp;&nbsp;&nbsp;&gt;</span>
    </a>
</div>
++++

Everyone has to start somewhere, and if you're new to Gradle, this is where to begin.

To get started using Gradle:

  }
}

static absl::StatusOr<std::vector<XlaCompiler::Argument>>
BuildXlaCompilerArgumentFromTensorSpec(
    const FunctionBody* fbody, absl::Span<int const> must_be_constant_idxs,
    absl::Span<const Tensor* const> inputs,
    absl::Span<VariableInfo const> variable_args,
    absl::Span<const ArgShapeAndDType> flat_arg_shape_and_dtype) {
  TF_RET_CHECK(fbody != nullptr);
  auto& input_args = fbody->record->fdef().signature().input_arg();

    private final Serializer<LogLevel> logLevelSerializer;
    private final Serializer<List<StyledTextOutputEvent.Span>> spanSerializer;

    public StyledTextOutputEventSerializer(Serializer<LogLevel> logLevelSerializer, Serializer<List<StyledTextOutputEvent.Span>> spanSerializer) {
        this.logLevelSerializer = logLevelSerializer;
        this.spanSerializer = spanSerializer;
    }

    @Override

<div class="termy">

```console
$ python ./scripts/docs.py live

<span style="color: green;">[INFO]</span> Serving on http://127.0.0.1:8008
<span style="color: green;">[INFO]</span> Start watching changes
<span style="color: green;">[INFO]</span> Start detecting changes
```

</div>

Das stellt die Dokumentation unter `http://127.0.0.1:8008` bereit.

==============================================================================*/

#include "tensorflow/c/experimental/saved_model/core/revived_types/flat_tensor_function.h"

#include <memory>
#include <string>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/immediate_execution_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"

#include "tensorflow/c/experimental/gradients/not_differentiable.h"

namespace tensorflow {
namespace gradients {
Status NotDifferentiableGradientFunction::Compute(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> grad_outputs,
    absl::Span<AbstractTensorHandle*> grad_inputs) {
  for (int i = 0; i < grad_inputs.size(); i++) {
    grad_inputs[i] = nullptr;
  }
  return absl::OkStatus();
}

class NotDifferentiableGradientFunction : public GradientFunction {
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override;
};
// Shorthand for registry->Register(op, new NotDifferentiableGradientFunction)

																	<span class="badge bg-primary"><la:message
                                                                            key="labels.enabled"/></span>
                                                    </c:if> <c:if test="${data.available=='false'}">
																	<span class="badge bg-gray"><la:message

  if (attr_.full_type() == "list(string)") {
    return "absl::Span<string const>";
  }

  // Normal path: translate base type to C++ ...
  string c_base_type = attr_.base_type();
  if (attr_.base_type() == "type") {
    c_base_type = "DataType";
  } else if (attr_.base_type() == "shape") {
    c_base_type = "const PartialTensorShape";
  }

  // ... and wrap in a Span<> if it's a list.
  if (attr_.is_list()) {

  }
};

class PassThroughGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);
    CHECK_EQ(grad_inputs.size(), 1);
    grad_inputs[0] = grad_outputs[0];
    if (grad_inputs[0]) {