return %1 : tensor<1x3x!quant.uniform<i8:f32, 5.000000e+0>>
    }
  )mlir";

  const OwningOpRef<ModuleOp> module_op =
      ParseModuleOpString(kQuantizedDotGeneral);
  ASSERT_TRUE(module_op);

  const QuantizationReport report(*module_op);
  const QuantizationResults& results = report.GetQuantizationResults();

func.func @equal_broadcast(%arg0: tensor<1xi32>, %arg1: tensor<1x2xi32>) -> tensor<1x2xi1> {
  // CHECK-DAG: %[[LHS_BCAST:.+]] = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<1> : tensor<1xi64>}>
  // CHECK-NEXT: mhlo.compare EQ, %[[LHS_BCAST]], %arg1
  %0 = "tf.Equal"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi1>
  func.return %0: tensor<1x2xi1>
}

// CHECK-LABEL: func @equal_broadcast_no_incompatible_shapes_error

    return %0 : tensor<1x3xf32>
  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>

  // CHECK: %[[WHILE_RESULT:.*]]:2 = mhlo.while(%[[ARG0:.*]] = %[[INT0]], %[[ARG1:.*]] = %[[INT1]])
  // CHECK-SAME: tensor<2x2xi8>, tensor<2x2xi8>

  // CHECK: cond

  // CHECK: do
  // CHECK: mhlo.return %[[ARG0]], %[[ARG1]] : tensor<2x?xi8>, tensor<?x2xi8>

  %2:2 = "tf.WhileRegion"(%0, %1) ({
  ^bb0(%carg0: tensor<2x2x!tf_type.qint8>, %carg1: tensor<2x2x!tf_type.qint8>):

// CHECK: return %[[UNIFORM_QUANTIZE_1]] : tensor<1x3x!quant.uniform<i8:f32, {{.*}}>>

// CHECK-PER-TENSOR: func.func private @quantized_dot_general_fn(%[[ARG_1:.+]]: tensor<1x2x!quant.uniform<i8:f32, {{.*}}>>, %[[ARG_2:.+]]: tensor<2x3x!quant.uniform<i8<-127:127>:f32, {{.*}}>>) -> tensor<1x3x!quant.uniform<i8:f32, {{.*}}>> attributes {_from_xla_call_module}

  %1 = "quantfork.qcast"(%0) {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>
  %2 = "quantfork.dcast"(%1) : (tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>) -> tensor<1x3xf32>
  return %2 : tensor<1x3xf32>
}
// CHECK: %[[XLA_CALL_MODULE_0:.+]] = "tf.XlaCallModule"() <{{{.*}}}> {{{.*}}} : () -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>

// CHECK-NEXT: %[[q2:.*]] = "tfl.quantize"(%[[cst_0]]) <{qtype = tensor<1x3x!quant.uniform<i8<-127:127>:f32:0, {{.*}}>>}> {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i8<-127:127>:f32:0, {{.*}}>>
// CHECK-NEXT: %[[dq2:.*]] = "tfl.dequantize"(%[[q2]]) : (tensor<1x3x!quant.uniform<i8<-127:127>:f32:0, {{.*}}>>) -> tensor<1x3xf32>

    // CHECK: %[[Q3:.*]] = "quantfork.qcast"(%[[ARG3]]) {volatile} : (tensor<1x3xf32>) -> tensor<1x3x!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    // CHECK: %[[SELECT:.*]] = stablehlo.select %[[ARG2]], %[[CALL]], %[[Q3]] : tensor<1x3xi1>, tensor<1x3x!quant.uniform<i8:f32, 0.13170163023705575:-1>>

  // CHECK-LABEL: @qu8
  // CHECK: <{qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>, value = dense<1> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>
  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>, value = dense<1> : tensor<3xi8>} : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
  func.return %0 : tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
}

  // CHECK-LABEL: @qu8
  // CHECK: <{qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>, value = dense<1> : tensor<3xi8>}> : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.000000e+00>>
  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>, value = dense<1> : tensor<3xi8>} : () -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
  func.return %0 : tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>>
}