%0 = stablehlo.uniform_quantize %arg : (tensor<2x2x!quant.uniform<i8:f32, 2.000000e+0:16>>) -> tensor<2x2x!quant.uniform<i8:f32, 3.000000e+0:127>>
  return %0 : tensor<2x2x!quant.uniform<i8:f32, 3.000000e+0:127>>
}
// CHECK-LABEL: uniform_quantize_op_quantized_input
// CHECK: stablehlo.uniform_quantize
// CHECK-NOT: tfl.quantize

// -----

}

// Returns kernel input feature dimension of TFLite convolutions.
int64_t GetConvolutionKernelInputFeatureDimension(bool is_depthwise) {
  return is_depthwise ? 0 : 3;
}

// stablehlo.uniform_quantize -> tfl.quantize
// TODO: b/322428814 - Add StableHLO quantizer integration tests for ODML.
class RewriteUniformQuantizeOp
    : public OpRewritePattern<stablehlo::UniformQuantizeOp> {

          'target_opset': quant_opts_pb2.UNIFORM_QUANTIZED,
          'input_shape_dynamic': False,
          'enable_per_channel_quantization': False,
      },
      {
          'testcase_name': 'dilation_with_bias_and_bn_and_relu6_to_uq',
          'activation_fn': nn_ops.relu6,
          'has_bias': True,
          'has_batch_norm': True,
          'target_opset': quant_opts_pb2.UNIFORM_QUANTIZED,

// -----

func.func @testUniformQuantize(%arg0: tensor<*xf32>, %scales: tensor<2xf32>, %zps: tensor<i32>) -> tensor<*x!tf_type.qint8> {
  // expected-error @below {{'tf.UniformQuantize' op quantization_axis is -1, scales must have 0 rank.}}
   %0 = "tf.UniformQuantize"(%arg0, %scales, %zps) {
    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64