if (offset >= pos + data.size()) {
      // The requested offset is at or beyond the end of the file. This can
      // happen if `offset` is not block-aligned, and the read returns the last
      // block in the file, which does not extend all the way out to `offset`.
      std::stringstream os;
      os << "EOF at offset " << offset << " in file " << filename
         << " at position " << pos << " with data size " << data.size();

    size_t size = TFE_TensorHandleDeviceMemorySize(copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    int64_t dims[] = {2, 2};
    TFE_TensorHandle* copy_aliased = TFE_NewTensorHandleFromDeviceMemory(
        ctx, name, TF_FLOAT, dims, 2, data, size, &Deleter, copy, status);
    CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TFE_TensorHandle* on_host =

  // fetcher calls that the cache makes.
  calls.push_back(0);
  // Cache miss - drains an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  // Cache hit - does not drain an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  calls.push_back(block_size);
  // Cache miss followed by cache hit.
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));

_site_packages_dirs = []
if _site.ENABLE_USER_SITE and _site.USER_SITE is not None:
  _site_packages_dirs += [_site.USER_SITE]
_site_packages_dirs += [p for p in _sys.path if "site-packages" in p]
if "getsitepackages" in dir(_site):
  _site_packages_dirs += _site.getsitepackages()

if "sysconfig" in dir(_distutils):
  _site_packages_dirs += [_distutils.sysconfig.get_python_lib()]

  ImmediateTensorHandlePtr dim(imm_ctx->CreateLocalHandle(minus_1.get()));
  AbstractTensorHandle* expand_dims_outputs;
  TF_RETURN_IF_ERROR(
      ops::ExpandDims(ctx, vec, dim.get(), &expand_dims_outputs, "ExpandDims"));
  TF_RETURN_IF_ERROR(
      ops::Mul(ctx, expand_dims_outputs, mat, &outputs[0], "Mul"));
  expand_dims_outputs->Unref();
  return absl::OkStatus();
}

class SparseSoftmaxCrossEntropyWithLogitsGradientFunction

  virtual Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual Status SetAttrShape(const char* attr_name, const int64_t* dims,
                              const int num_dims) = 0;
  virtual Status SetAttrShape(const char* attr_name,
                              const PartialTensorShape shape);
  virtual Status SetAttrFunction(const char* attr_name,

  static char empty;
  int64_t nelems = 1;
  std::vector<int64_t> dims;
  dims.reserve(shape.dims());
  for (int i = 0; i < shape.dims(); ++i) {
    dims.push_back(shape.dim_size(i));
    nelems *= shape.dim_size(i);
  }
  CHECK_EQ(nelems, 0);
  return TF_NewTensor(
      dtype, reinterpret_cast<const int64_t*>(dims.data()), shape.dims(),
      reinterpret_cast<void*>(&empty), 0, [](void*, size_t, void*) {}, nullptr);

// If the number of dimensions is unknown, `num_dims` must be set to
// -1 and `dims` can be null.  If a dimension is unknown, the
// corresponding entry in the `dims` array must be -1.
TF_CAPI_EXPORT extern void TFE_OpSetAttrShape(TFE_Op* op, const char* attr_name,
                                              const int64_t* dims,
                                              const int num_dims,

    return nullptr;
  }
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);
    Status status = tensorflow::PartialTensorShape::MakePartialShape(
        reinterpret_cast<int64_t*>(shape.dim_sizes), shape.num_dims,
        &partial_shape);
    if (!status.ok()) {
      tsl::Set_TF_Status_from_Status(s, status);
      return nullptr;

    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error = 1e-2);

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error = 1e-2);

Model BuildGradModel(Model forward, GradientRegistry registry);

}  // namespace internal
}  // namespace gradients
}  // namespace tensorflow