absl::Span<AbstractTensorHandle*> grad_inputs) override {
    for (int i = 0; i < grad_outputs.size(); i++) {
      auto grad_input = grad_outputs[i];
      // TODO(srbs): Should we add a copy contructor to AbstractTensorHandle
      // that takes care of this similar to `Tensor`?
      if (grad_input) {
        grad_input->Ref();
      }
      grad_inputs[i] = grad_input;
    }

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // The device of var_handle0 is local device which is the same before and
  // after cluster update. Remove resource with valid device should succeed.
  TFE_Op* op = TFE_NewOp(ctx, "DestroyResourceOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddInput(op, var_handle0, status);
  TFE_OpSetDevice(op, dev0_name, status);

        "//tensorflow/c/experimental/filesystem:filesystem_interface",
    ],
)

# Since building pip package and API tests require a filesystem, we provide a
# static registration target that they should link against.
cc_library(
    name = "posix_filesystem_static",
    srcs = ["posix_filesystem_static.cc"],
    visibility = ["//visibility:public"],
    deps = [
        ":posix_filesystem_impl",

      tensorflow::unwrap(h);
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.
  if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
    return tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(
               unwrapped_handle)
        ->DevicePointer();
  }
  // TODO(b/175427838): It would be nice to be able to use tensorflow::isa here.

    if environ_cp.get('TF_CUDA_CLANG') == '1':
      # Set up which clang we should use as the cuda / host compiler.
      clang_cuda_compiler_path = set_clang_cuda_compiler_path(environ_cp)
      clang_version = retrieve_clang_version(clang_cuda_compiler_path)
      disable_clang_offsetof_extension(clang_version)
    else:
      # Set up which gcc nvcc should use as the host compiler
      # No need to set this on Windows

        "@local_xla//xla/pjrt:pjrt_c_api_client",
        "@local_xla//xla/pjrt:pjrt_client",
        "@local_xla//xla/pjrt/c:pjrt_c_api_hdrs",
        "@local_xla//xla/pjrt/c:pjrt_c_api_helpers",
    ],
)

# Plugin should include this target to avoid linking the C API implementation.
cc_library(
    name = "c_api_hdrs",
    hdrs = ["c_api.h"],
    visibility = ["//visibility:public"],
    deps = [

    if (prefix == nullptr) {
      child_scope_name = "gradients";
    } else {
      prefix_cmp = string(prefix) + "/";
      // The operation should fail if the provided name prefix has already been
      // used in this graph
      for (const auto& pair : g->name_map) {
        const string& name = pair.first;
        if ((name == prefix) || absl::StartsWith(name, prefix_cmp)) {
          status->status = InvalidArgument(

// The elements of `dims` will point to addresses in `storage` which must be
// large enough to hold at least `storage_size` int64_ts.  Ideally, `num_shapes`
// would be set to TF_AttrMetadata.list_size and `storage_size` would be set to
// TF_AttrMetadata.total_size from TF_OperationGetAttrMetadata(oper,
// attr_name).
//
// Fails if storage_size is insufficient to hold the requested shapes.
TF_CAPI_EXPORT extern void TF_OperationGetAttrShapeList(

        tensorflow::unwrap(op_to_reset);
    op->Clear();
    status->status = op->Reset(op_or_function_name, raw_device_name);
  } else {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "op_to_reset should not be nullptr");
  }
}

void TFE_ContextEnableGraphCollection(TFE_Context* ctx) {
  tensorflow::unwrap(ctx)->SetShouldStoreGraphs(true);
}

void TFE_ContextDisableGraphCollection(TFE_Context* ctx) {

Also, please include a link to a GraphDef or the model if possible.

**Any other info / logs**

Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files