std::vector<PartialTensorShape> shapes;
  shapes.reserve(num_shapes);
  for (int i = 0; i < num_shapes; ++i) {
    if (num_dims[i] < 0) {
      shapes.emplace_back();
    } else {
      shapes.emplace_back(absl::Span<const int64_t>(
          reinterpret_cast<const int64_t*>(dims[i]), num_dims[i]));
    }
  }
  desc->node_builder.Attr(attr_name, shapes);
}

    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);
    if (shape.num_dims == InferenceContext::kUnknownRank) {
      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));
    }
  }

}

TEST(CAPI, ShapeInferenceError) {
  // TF_FinishOperation should fail if the shape of the added operation cannot
  // be inferred.
  TF_Status* status = TF_NewStatus();
  TF_Graph* graph = TF_NewGraph();

  // Create this failure by trying to add two nodes with incompatible shapes
  // (A tensor with shape [2] and a tensor with shape [3] cannot be added).
  const char data[] = {1, 2, 3};
  const int64_t vec2_dims[] = {2};

"$python" -m pip install *.whl $TFCI_PYTHON_VERIFY_PIP_INSTALL_ARGS
if [[ "$TFCI_WHL_IMPORT_TEST_ENABLE" == "1" ]]; then
  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.

    *   Start enforcing input shape assumptions when calling Functional API
        Keras models. This may potentially break some users, in case there is a
        mismatch between the shape used when creating `Input` objects in a
        Functional model, and the shape of the data passed to that model. You
        can fix this mismatch by either calling the model with correctly-shaped

{* ../../docs_src/security/tutorial005_an_py310.py hl[106,108:116] *}

## Verify the `username` and data shape { #verify-the-username-and-data-shape }

We verify that we get a `username`, and extract the scopes.

            EndpointPair.ordered(1, 1),
            EndpointPair.ordered(1, 3),
            EndpointPair.ordered(1, 2))
        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateStarShapedGraph() {
    putEdge(2, 1);

            EndpointPair.unordered(1, 1),
            EndpointPair.unordered(1, 3))
        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateTShapedGraph() {
    putEdge(2, 1);

You just pass it to `Depends` and **FastAPI** knows how to do the rest.

///

## Share `Annotated` dependencies { #share-annotated-dependencies }

In the examples above, you see that there's a tiny bit of **code duplication**.

            EndpointPair.unordered(1, 1),
            EndpointPair.unordered(1, 3))
        .inOrder();
  }

  /**
   * Populates the graph with nodes and edges in a star shape with node `1` in the middle.
   *
   * <p>Note that the edges are added in a shuffled order to properly test the effect of the
   * insertion order.
   */
  private void populateTShapedGraph() {
    putEdge(2, 1);