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);
}

  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);

  // Infer shape when everything is known.
  CheckOutputShapes(matmul_op,
                    /*input_shapes*/ {make_shape({3, 2}), make_shape({2, 4})},
                    /*input_tensors*/ {},
                    /*expected_shape*/ make_shape({3, 4}));

  // Infer shape when second operand has unknown shape.
  CheckOutputShapes(matmul_op,

    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};

    TF_ShapeAndTypeList** shape_list_array, int num_items);

// Infer shapes for the given `op`. The arguments mimic the arguments of the
// `shape_inference::InferenceContext` constructor. Note the following:
//   - The inputs of the `op` are not used for shape inference. So, it is
//     OK to not have the inputs properly set in `op`. See `input_tensors`
//     if you want shape inference to consider the input tensors of the
//     op for shape inference.

// setting a shape of [-1, 2] with an existing shape [2, -1] would set
// a final shape of [2, 2] based on shape merging semantics.
//
// Returns an error into `status` if:
//   * `output` is not in `graph`.
//   * An invalid shape is being set (e.g., the shape being set
//     is incompatible with the existing shape).
TF_CAPI_EXPORT extern void TF_GraphSetTensorShape(TF_Graph* graph,

"$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.

inspected and debugged and it is intended to be used during the development
phase.

As part of the differences that make Eager mode easier to debug, the [shape
inference
functions](https://www.tensorflow.org/guide/create_op#define_the_op_interface)
are skipped, and any checks implemented inside the shape inference code are not
executed.

The security impact of skipping those checks should be low, since the attack

    *   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

namespace tensorflow {

// Set the shapes and types of the output's handle.
//
// The lengths of the arrays pointed to by `shapes`, `ranks`, and `types` must
// all be equal to `num_shapes_and_types`. If `ranks[i] != -1`, (i.e., if the
// rank is known), then it must be equal to the length of `shapes[i]`; if
// `ranks[i] == 1`, then `shapes[i]` may be nullptr.
//