TFE_TensorDebugInfo* debug_info) {
  delete debug_info;
}

TF_CAPI_EXPORT extern int TFE_TensorDebugInfoOnDeviceNumDims(
    TFE_TensorDebugInfo* debug_info) {
  return debug_info->dev_dims.size();
}

TF_CAPI_EXPORT extern int64_t TFE_TensorDebugInfoOnDeviceDim(
    TFE_TensorDebugInfo* debug_info, int dim_index) {
  return debug_info->dev_dims[dim_index];
}

  // Returns a map from variable names to their shapes.  Slices of a partitioned
  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.
  const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const;

/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**
/tensorflow/lite/tools/make/gen/**
/api_init_files_list.txt
/estimator_api_init_files_list.txt
*.whl

  // Step 3: Call `TF_InitPlugin`
  TF_FilesystemPluginInfo info;
  memset(&info, 0, sizeof(info));
  auto TF_InitPlugin =
      reinterpret_cast<int (*)(TF_FilesystemPluginInfo*)>(dso_symbol);
  TF_InitPlugin(&info);

  // Step 4: Do the actual registration
  return filesystem_registration::RegisterFilesystemPluginImpl(&info);
}

    TF_SetStatus(status, TF_OK, "");
    return 0;
  }
  if (!IsCacheEnabled() || (n > max_bytes_)) {
    // The cache is effectively disabled, so we pass the read through to the
    // fetcher without breaking it up into blocks.
    return block_fetcher_(filename, offset, n, buffer, status);
  }
  // Calculate the block-aligned start and end of the read.
  size_t start = block_size_ * (offset / block_size_);

void AllocateLoggingDevice(const char* name, bool* arrived_flag,
                           bool* executed_flag, TFE_CustomDevice** device,
                           void** device_info);
TFE_TensorHandle* UnpackTensorHandle(TFE_TensorHandle* logged_tensor_handle,
                                     TF_Status* status);

    see! value: render: shell validations: required: true

-   type: textarea id: logs attributes: label: Relevant log output description:
    Please copy and paste any relevant log output. This will be automatically

to the `../wheel_test/` directory.


### How it Works in the Presubmit Job

`_requirements_lock` files will be generated by the presubmit job. A detailed
description will be provided once it's integrated into presubmit.

### test_import_api_packages

This Python test verifies whether the API v2 packages can be imported from the
current build. It utilizes the `_api/v2/api_packages.txt` list of packages from

      TestScalarTensorHandle<T, datatype>(eager_ctx, value);
  *tensor =
      unwrap(TF_CreateAbstractTensorFromEagerTensor(input_eager, status.get()));
  return absl::OkStatus();
}

// Places data from `t` into *result_tensor.
Status GetValue(AbstractTensorHandle* t, TF_Tensor** result_tensor);
}  // namespace tensorflow

  LOG(INFO) << "There are " << num_devices << " devices.";
  for (int i = 0; i < num_devices; ++i) {
    const char* device_name = TF_DeviceListName(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    const char* device_type = TF_DeviceListType(device_list, i, s);
    CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
    LOG(INFO) << "Device " << i << " has name " << device_name << ", type "