TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Try to set an invalid shape (cannot change 2x3 to a 2x5).
  dims[1] = 5;
  TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Test for a scalar.
  TF_Operation* three = ScalarConst(3, graph, s);

  TFE_DeleteContextOptions(opts);

  TF_AbstractFunction* f = TF_FinalizeFunction(ctx, nullptr, status.get());
  ASSERT_EQ(nullptr, f);
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  TF_DeleteExecutionContext(ctx);
}

TEST_P(UnifiedCAPI, TF_AbstractOpSetOpTypeAfterFinishingOpBuildingRaises) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(

  TF_Tensor* t = TFE_TensorHandleResolve(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(t, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  const char* device_name = TFE_TensorHandleDeviceName(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_name, nullptr);

To set a different version, use the `TF_PYTHON_VERSION` environment variable,
e.g.

```
export TF_PYTHON_VERSION=3.11
```

To specify the version via a Bazel command argument, use the following:

```
--repo_env=TF_PYTHON_VERSION=3.11
```

## Requirements updater

Requirements updater is a standalone tool, intended to simplify process of

  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_type, nullptr);
  ASSERT_EQ("Invalid handle", string(TF_Message(status.get())));

  TF_SetStatus(status.get(), TF_OK, "");

  int device_id = TFE_TensorHandleDeviceID(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));
  ASSERT_EQ(device_id, -1);

/// `const`.
///
/// For consistency, the arguments on all these functions follow the same
/// pattern: first we have the opaque pointer argument ("this" above), then the
/// input arguments, then the in-out arguments (if any) and we finish the
/// argument list with the out arguments. We only use the return type for an out
/// parameter if that is a plain C type, as this ensures ABI compatibility

#include <direct.h>
#define mkdir(name, mode) _mkdir(name)

// Windows defines the following macros to convert foo to fooA or fooW,
// depending on the type of the string argument. We don't use these macros, so
// undefine them here.
#undef CopyFile
#undef DeleteFile
#undef TranslateName
#endif  // defined(PLATFORM_WINDOWS)

# input the branch name and paste the cherry-pick commit and click Run. A PR
# will be created.

name: Release Branch Cherrypick
on:
  workflow_dispatch:
    inputs:
      # We use this instead of the "run on branch" argument because GitHub looks
      # on that branch for a workflow.yml file, and we'd have to cherry-pick
      # this file into those branches.
      release_branch:
        description: 'Release branch name (e.g. r2.9)'

    TF_AttrBuilder* builder, const char* device_type, TF_Status* status);

// For argument number input_index, fetch the corresponding number_attr that
// needs to be updated with the argument length of the input list.
// Returns nullptr if there is any problem like op_name is not found, or the
// argument does not support this attribute type.
TF_CAPI_EXPORT extern const char* TF_GetNumberAttrForOpListInput(

  virtual ImmediateExecutionContext* GetContext() const = 0;

  // Following two methods are used to support custom device.
  // Return true if the inputs contain custom device tensor handle. It means
  // that the argument need to be handled by a custom device.
  virtual bool HasCustomDeviceInput() const = 0;

  virtual const tensorflow::OpDef* OpDef() const = 0;

  virtual Status InputLength(const char* input_name, int* length) = 0;