shapes.resize(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (proto_lens[i] > std::numeric_limits<int>::max()) {
      status->status = InvalidArgument(
          "length of element ", i, " in the list (", proto_lens[i],
          " bytes) is too large to be parsed by the protocol buffer library");
      return;
    }
    if (!shapes[i].ParseFromArray(protos[i], static_cast<int>(proto_lens[i]))) {

  std::vector<int32_t> data(1, 0);
  xla::Shape shape = xla::ShapeUtil::MakeShape(xla::S32, {1});

  auto buffer = c_api_client->pjrt_c_client()->client->BufferFromHostBuffer(
      data.data(), shape.element_type(), shape.dimensions(),
      /*byte_strides=*/std::nullopt,
      xla::PjRtClient::HostBufferSemantics::kImmutableOnlyDuringCall, nullptr,
      c_api_client->pjrt_c_client()->client->addressable_devices()[0]);

  error_count = 0

  date_time = datetime.datetime
  attrib = {'name': 'Bazel Invocations', 'time': '0.0',
            'timestamp': date_time.isoformat(date_time.utcnow())}
  testsuites = ElemTree.Element('testsuites')
  testsuite = ElemTree.SubElement(testsuites, 'testsuite')
  for url, invocation_results in result_store_dict.items():
    invocation_id = url.rsplit('/')[-1]
    if verbose:

// entries (e.g., the return value of TF_GraphGetTensorNumDims).
//
// If the number of dimensions in the shape is unknown or the shape is
// a scalar, `dims` will remain untouched. Otherwise, each element of
// `dims` will be set corresponding to the size of the dimension. An
// unknown dimension is represented by `-1`.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.

  TF_EXPECT_OK(ReadCache(&cache, "", 2 * block_size, 1, &out));
  // Element at block_size was evicted.  Reading this element will also cause
  // the LRU element (at 0) to be evicted.
  calls.push_back(block_size);
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));
  // Element at 0 was evicted again.
  calls.push_back(0);
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
}

  std::unique_ptr<NowSecondsEnv> env(new NowSecondsEnv);
  tf_gcs_filesystem::ExpiringLRUCache<int> cache(
      1, 0, [&env]() { return env->NowSeconds(); });
  env->SetNowSeconds(1);
  // Verify that replacement of an existing element works, and updates the
  // timestamp of the entry.
  cache.Insert(key, 41);
  env->SetNowSeconds(2);
  cache.Insert(key, 42);
  // 1 second after the most recent insertion, the entry is still valid.

    // The following input string length is large enough to make sure that
    // copy to tstring in large mode.
    std::string source =
        "This is the " + std::to_string(i + 1) + "th. data element\n";
    TF_TString_Copy(&data[i], source.c_str(), source.length());
  }

  TF_DeleteTensor(t);
}

}  // namespace
}  // namespace tensorflow

// TODO(josh11b): Test:

  // once) and produces the gradient of the target tensors with respect to the
  // source tensors. The output gradients are used if not empty and not
  // null. The result is populated with one tensor per target element.
  // When running backward functions, builds zeros-like tensors for
  // incoming grads which are nullptrs, unless `build_default_zeros_grads`
  // is set to false.
  Status ComputeGradient(

  // tensors with respect to the source tensors. The output gradients are used
  // if not empty and not null. The result is populated with one tensor per
  // target element.
  Status ComputeGradient(
      AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> targets,
      absl::Span<AbstractTensorHandle* const> sources,
      absl::Span<AbstractTensorHandle* const> output_gradients,

  ///     of type `TF_Status*`.
  ///
  /// If `statuses` is not null, plugins must fill each element with detailed
  /// status for each file, as if calling `path_exists` on each one. Core
  /// TensorFlow initializes the `statuses` array and plugins must use
  /// `TF_SetStatus` to set each element instead of directly assigning.
  ///
  /// DEFAULT IMPLEMENTATION: Checks existence of every file. Needs