%0 = "tf.EnsureShape"(%arg0) {shape = #tf_type.shape<10x20>} : (tensor<10x20xf32>) -> tensor<10x20xf32>
  %1 = "tf.EnsureShape"(%arg0) {shape = #tf_type.shape<?x20>} : (tensor<10x20xf32>) -> tensor<10x20xf32>
  // Failing case which should not be folded.
  // CHECK: %[[NF:.*]] = "tf.EnsureShape"(%arg0) <{shape = #tf_type.shape<20x10>}>
  %2 = "tf.EnsureShape"(%arg0) {shape = #tf_type.shape<20x10>} : (tensor<10x20xf32>) -> tensor<20x10xf32>

// Extracts shape from XlaArgument as TensorShape. If shape is a xla::Shape,
// that is converted to a TensorShape.
absl::StatusOr<TensorShape> GetTensorShapeFromXlaArgument(
    const XlaArgument& arg) {
  if (absl::holds_alternative<xla::Shape>(arg.shape)) {
    TensorShape arg_shape;
    TF_RETURN_IF_ERROR(
        XLAShapeToTensorShape(std::get<xla::Shape>(arg.shape), &arg_shape));
    return arg_shape;

  %begin0 = arith.constant dense<0> : tensor<4xi64>
  %shape0 = arith.constant dense<[2,3,4,4]> : tensor<4xi64>
  %begin1 = arith.constant dense<1> : tensor<4xi64>
  %shape1 = arith.constant dense<[1,2,3,4]> : tensor<4xi64>
  %0 = "tfl.slice"(%arg0, %begin0, %shape0) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<2x3x4x4xf32>

  }

  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;
}

        return mlir::UnrankedTensorType::get(
            mlir::TF::ResourceType::get(builder.getContext()));
      }
    } else if (auto shape = node.attrs().Find("_output_shapes")) {
      if (shape->has_list() && shape->list().shape_size() == 1) {
        return ConvertToMlirTensorType(shape->list().shape().at(0), dtype,
                                       &builder);
      }
    }
  }

  // CHECK-NEXT: %[[SHAPE0:.*]] = shape.shape_of %arg0 : tensor<?xi1> -> tensor<1xindex>
  // CHECK-NEXT: %[[SHAPE1:.*]] = shape.shape_of %arg1 : tensor<?x?x8xi32> -> tensor<3xindex>
  // CHECK-NEXT: %[[SHAPE2:.*]] = shape.shape_of %arg2 : tensor<?x?x8xi32> -> tensor<3xindex>
  // CHECK-NEXT: %[[SHAPEEQ1:.*]] = shape.cstr_eq %[[SHAPE1]], %[[SHAPE2]] : tensor<3xindex>, tensor<3xindex>

  return
}
```
### `-tf-tpu-annotate-dynamic-shape-inputs`

_Annotate the inputs returned by TPUCopyWithDynamicShapeOp with dynamic shape_

This pass looks for the usage of the result of TPUCopyWithDynamicShapeOp
and sets the shape of these inputs to be dynamic shaped. This will ensure
that the generated HLO program is correctly reflecting the dynamic shape.
### `-tf-tpu-cleanup-cluster-attributes`

    ::flatbuffers::FlatBufferBuilder &_fbb,
    const std::vector<int32_t> *shape = nullptr,
    tflite::TensorType type = tflite::TensorType_FLOAT32,
    bool has_rank = false) {
  auto shape__ = shape ? _fbb.CreateVector<int32_t>(*shape) : 0;
  return tflite::CreateVariantSubType(
      _fbb,
      shape__,
      type,
      has_rank);
}