}                                                                     \
    if (index < 0 || index >= list->response.size()) {                    \
      status->status = InvalidArgument("index out of bounds");            \
      return err_val;                                                     \
    }                                                                     \

  func.return %0 : tensor<i1>
}

// -----

func.func @testRoundInvalidInputType(%arg: tensor<?xi32>) -> tensor<?xi32> {
  // expected-error @+1 {{'tfl.round' op operand #0 must be tensor of 32-bit float values}}
  %0 = "tfl.round"(%arg) : (tensor<?xi32>) -> tensor<?xi32>
  func.return %0 : tensor<?xi32>
}

// -----

        return op.emitOpError()
               << "found mismatching batch dimensions for lhs shape " << x_ty
               << " and rhs shape " << y_ty;
      }
    }
  } else {
    if (!OpTrait::util::getBroadcastedShape(x_batches, y_batches,
                                            result_batch_shape))
      return op.emitOpError()
             << "found incompatible broadcast batch dimensions for lhs shape "

                                    rewriter))) {
      return failure();
    }

    ImplicitLocOpBuilder builder(gather_op.getLoc(), rewriter);
    // Clamp the start indices to ensure it is in bounds.
    auto max_start_indices = BuildIntArrayConstOp(
        builder, rewriter,
        llvm::SmallVector<int64_t>(
            {operand_type.getDimSize(0) - slice_sizes_vector[0],

	// The scopes field is used to avoid unnecessary parentheses
	// around expressions that use > (or >>). It is incremented if
	// we output a parenthesis or something else that means that >
	// or >> won't be treated as ending a template. It starts out
	// as 1, and is set to 0 when we start writing template
	// arguments. We add parentheses around expressions using > if
	// scopes is 0. The effect is that an expression with > gets

  // Test dimensions sizes.
  %d1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %d2 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>

  // Slice bounds.
  %0 = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
  %1 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>

        quant_parameters) {
  auto type = mlir::cast<TensorType>(value.getType());

  // TFLite requires tensor shape only for the inputs and constants.
  // However, we output all known shapes for better round-tripping
  auto check_shape =
      [&](llvm::ArrayRef<int64_t> shape_ref) -> mlir::LogicalResult {
    auto is_out_of_range = [](int64_t dim) {
      return dim > std::numeric_limits<int32_t>::max();
    };

	w.WriteHeader(code)
	fmt.Fprintln(w, error)
}

// NotFound replies to the request with an HTTP 404 not found error.
func NotFound(w ResponseWriter, r *Request) { Error(w, "404 page not found", StatusNotFound) }

// NotFoundHandler returns a simple request handler
// that replies to each request with a “404 page not found” reply.
func NotFoundHandler() Handler { return HandlerFunc(NotFound) }

	{AAESIMC, yaes, Pq, opBytes{0x38, 0xdb, 0}},
	{AAESKEYGENASSIST, yxshuf, Pq, opBytes{0x3a, 0xdf, 0}},
	{AROUNDPD, yxshuf, Pq, opBytes{0x3a, 0x09, 0}},
	{AROUNDPS, yxshuf, Pq, opBytes{0x3a, 0x08, 0}},
	{AROUNDSD, yxshuf, Pq, opBytes{0x3a, 0x0b, 0}},
	{AROUNDSS, yxshuf, Pq, opBytes{0x3a, 0x0a, 0}},
	{APSHUFD, yxshuf, Pq, opBytes{0x70, 0}},
	{APCLMULQDQ, yxshuf, Pq, opBytes{0x3a, 0x44, 0}},