Operation* filter_op = filter_value.getDefiningOp();
    auto filter_uniform_quantized_type =
        GetElementType(filter_value).cast<UniformQuantizedPerAxisType>();
    auto filter_constant_value_attr = cast<DenseIntElementsAttr>(
        cast<stablehlo::ConstantOp>(filter_value.getDefiningOp()).getValue());
    const DenseIntElementsAttr new_filter_value_attr =

#include "tensorflow/core/common_runtime/eager/tensor_handle.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_server_lib.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/protobuf.h"

  }
}

LogicalResult SpaceToBatchNDOp::verify() {
  SpaceToBatchNDOp op = *this;
  const auto input_type = op.getInput().getType().cast<TensorType>();
  const auto block_shape_type = op.getBlockShape().getType().cast<TensorType>();
  const auto paddings_type = op.getPaddings().getType().cast<TensorType>();

  // Check that block_shape has rank 1.
  if (!IsOfRankOrUnranked(op.getBlockShape(), 1)) {

// CHECK-NEXT:        buffer: 4
// CHECK-NEXT:      } ],
// CHECK-NEXT:      signature_defs: [  ]
// CHECK-NEXT:    }

  %0 = "tfl.cast" (%arg0) : (tensor<4x5xbf16>) -> tensor<4x5xf32> loc("cast1")
  %1 = "tfl.cast" (%0) : (tensor<4x5xf32>) -> tensor<4x5xbf16> loc("cast2")
  func.return %1 : tensor<4x5xbf16>

  // types match the result types.
  auto yield = cast<YieldOp>(region.front().getTerminator());
  auto updated_results = llvm::to_vector<4>(yield.getOperands());

  // If the yield types do not match the IfRegion result types, add appropriate
  // casts.
  rewriter.setInsertionPoint(yield);
  for (auto it : llvm::zip(op.getResultTypes(), updated_results)) {

// Ensure cast with bfloat16 roundtrip exactly

func.func @main(tensor<4x5xbf16>) -> tensor<4x5xbf16> {
^bb0(%arg0: tensor<4x5xbf16>):
  // CHECK-LABEL: @main
  // CHECK:  (tensor<4x5xbf16>) -> tensor<4x5xf32>
  // CHECK-NEXT:  (tensor<4x5xf32>) -> tensor<4x5xbf16>
  %0 = "tfl.cast" (%arg0) : (tensor<4x5xbf16>) -> tensor<4x5xf32> loc("cast1")
  %1 = "tfl.cast" (%0) : (tensor<4x5xf32>) -> tensor<4x5xbf16> loc("cast2")

// CHECK-NEXT: %[[AXIS:.*]] = arith.constant 0 : i32
// CHECK-NEXT: %[[CAST0:.*]] = "tfr.cast"(%arg0) : (tensor<2x3xf32>) -> !tfr.tensor
// CHECK-NEXT: %[[CAST1:.*]] = "tfr.cast"(%arg1) : (tensor<2x3xf32>) -> !tfr.tensor
// CHECK-NEXT: %[[CAST2:.*]] = "tfr.cast"(%arg2) : (tensor<2x3xf32>) -> !tfr.tensor
// CHECK-NEXT: %[[EX0:.*]] = tfr.call @tf__expand_dims(%[[CAST0]], %[[AXIS]]) : (!tfr.tensor, i32) -> !tfr.tensor

// CHECK: ^bb2:
// CHECK:   [[CAST2:%.+]] = "tf.Cast"(%arg1) <{Truncate = false}> : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant>
// CHECK:   [[ELSE:%.+]] = call @testIfElse([[CAST2]]) : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>
// CHECK:   [[CAST3:%.+]] = "tf.Cast"([[ELSE]]) <{Truncate = false}> : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant<tensor<f32>>>

// division can result in strange behavior.
//
//      floordiv = cast(floordiv(cast(left), cast(right))))
//
//   %left_cast = cast(%left)
//   %right_cast = cast(%right)
//   %div = div(%left, %left)
//   %floored = floor(%div)
//   %floored_cast = cast(%floored)
//
// Required to manually specify the intermediate types.

		participle.CaseInsensitive("Timeword"),
	)

	validCases := []string{
		"count(*)",
		"sum(2 + s.id)",
		"sum(t)",
		"avg(s.id[1])",
		"coalesce(s.id[1], 2, 2 + 3)",

		"cast(s as string)",
		"cast(s AS INT)",
		"cast(s as DECIMAL)",
		"extract(YEAR from '2018-01-09')",
		"extract(month from '2018-01-09')",

		"extract(hour from '2018-01-09')",
		"extract(day from '2018-01-09')",