}

  std::vector<std::optional<std::vector<int>>> shapes;
  TF_RETURN_IF_ERROR(::tensorflow::ParseNodeShapes(input_shapes, shapes));

  for (const auto& shape : shapes) {
    if (!shape) {
      return absl::AbortedError("Missing input argument shapes");
    }
    parsed_shapes.push_back(SmallVector<int64_t>(shape->begin(), shape->end()));
  }
  return parsed_shapes;
}

// 'shape' is the original shape with padding to match result shape.
int64_t GetElementIndex(const std::vector<int64_t>& shape,
                        const std::vector<int64_t>& current_index) {
  int64_t ind = 0;
  int64_t mul = 1;
  for (int i = shape.size() - 1; i >= 0; --i) {
    ind += (current_index[i] % shape[i]) * mul;
    mul *= shape[i];
  }
  return ind;
}

TensorSpec::TensorSpec()
    : shape_(std::initializer_list<int64_t>()), dtype_(DT_FLOAT) {}

TensorSpec::TensorSpec(PartialTensorShape shape, DataType dtype)
    : shape_(std::move(shape)), dtype_(dtype) {}

TensorSpec::TensorSpec(const TensorSpecProto& proto)
    : shape_(proto.shape()), dtype_(proto.dtype()) {}

const PartialTensorShape& TensorSpec::shape() const { return shape_; }

bool CanBeRefined(Type type);

// Returns a new arg type based on the shape and element type. If there are
// dynamic bounds attribute to the arg, update the bounds based on the shape
// as well.
Type GetNewArgType(Type old_arg_type, ArrayRef<int64_t> shape,
                   Type element_type, mlir::MLIRContext* context);

// Refines all the shapes in a module, skipping the inference for all ops
// whose type is in ops_to_skip.

                                  PatternRewriter &rewriter) {
  // Fail for non-static shapes
  if (!pad_op.getOperand().getType().hasStaticShape() ||
      !pad_op.getResult().getType().hasStaticShape() ||
      !pad_op.getPaddingValue().getType().hasStaticShape()) {
    return rewriter.notifyMatchFailure(pad_op, "dynamic shapes not supported");
  }

  // Check if the operand is also a Pad op
  auto parent_pad =

      se::Stream* stream, se::DeviceMemoryBase* device_memory_base,
      const xla::Shape& shape,
      tsl::AsyncValueRef<std::unique_ptr<se::Event>>& done_event)
      : stream_(stream),
        device_memory_base_(device_memory_base),
        shape_(shape),
        done_event_(done_event) {}

  // Copies 'cpu_tensor' to `device_memory_base_` with `shape_`.
  // `device_tensor` is unused.

node: {
  name: "x"
  op: "Placeholder"
  attr: {
    key: "shape"
    value: {
      shape: {
        dim: {
          size: -1
        }
      }
    }
  }
  attr: {
    key: "dtype"
    value: {
      type: DT_INT32
    }
  }
}
node: {
  name: "y"
  op: "Placeholder"
  attr: {
    key: "shape"
    value: {
      shape: {
        dim: {
          size: -1
        }
      }

  // Fuse select(broadcast_to(input, shape), x, y) -> selectV2(input, x, y)
  // Also, fuse selectv2(broadcast_to(input, shape), x, y) -> selectV2(input, x, y)
  // It is safe to perform this transform here because-
  // the shapes of `pre_broadcast` and `dim` must be broadcast
  // compatible for the `broadcast_to` op to be valid.
  // And considering, `shape(post_broadcast)` == `shape(%input1)`,

		resources []config.ResourceSpec
		shapes    []config.UtilizationShapePoint
		wantErrs  field.ErrorList
	}{
		{
			name:   "no shapes",
			shapes: nil,
			wantErrs: field.ErrorList{
				{
					Type:  field.ErrorTypeRequired,
					Field: "scoringStrategy.shape",
				},
			},
		},
		{
			name:   "weight greater than max",
			shapes: defaultShape,
			resources: []config.ResourceSpec{

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-input-arrays=input0,input1 -tf-input-shapes=10:10 -tf-output-arrays=Add -o - | FileCheck --check-prefix=NONE %s