func.func @DontReorderReshapex2Add(%arg0: tensor<1x2x3x4x!quant.uniform<i8:f32, 3.0>>, %arg1: tensor<1x2x3x4x!quant.uniform<i8:f32, 5.0>>) -> tensor<6x4x!quant.uniform<i8:f32, 7.0>> {
  %shape = arith.constant dense<[6, 4]> : tensor<2xi32>
  %0 = "tfl.reshape"(%arg0, %shape) : (tensor<1x2x3x4x!quant.uniform<i8:f32, 3.0>>, tensor<2xi32>) -> tensor<6x4x!quant.uniform<i8:f32, 3.0>>

}

// -----

// CHECK-LABEL: @random_shuffle_1D_10240
func.func @random_shuffle_1D_10240(%input: tensor<10240xf32>) -> tensor<10240xf32> {
  // CHECK: mhlo.rng{{.*UNIFORM.*}}
  // CHECK: mhlo.sort
  // CHECK: mhlo.rng{{.*UNIFORM.*}}
  // CHECK: mhlo.sort
  %0 = "tf.RandomShuffle"(%input) : (tensor<10240xf32>) -> (tensor<10240xf32>)
  func.return %0: tensor<10240xf32>
}

// -----

// CHECK-LABEL:   func @const_quant
// CHECK-NOT:       "tf.Const"
func.func @const_quant() -> tensor<512x1x!quant.uniform<i8:f32, 0.013133913278579712>> {
  %0 = mhlo.constant() {value = dense<0> : tensor<512x1xi8>} : () -> tensor<512x1x!quant.uniform<i8:f32, 0.013133913278579712>>
  func.return %0 : tensor<512x1x!quant.uniform<i8:f32, 0.013133913278579712>>
}

// CHECK-LABEL:   func @convert_dot_quant_type(

      loc, builder->getI32IntegerAttr(upper_limit));

  return builder->create<mhlo::RngOp>(loc, lower, upper, shape_tensor,
                                      ::mlir::mhlo::RngDistribution::UNIFORM);
}

using WhileBodyFnType = llvm::function_ref<void(
    Location loc, Value iteration, ArrayRef<Value> old_values,
    SmallVectorImpl<Value> *new_values, OpBuilder *builder)>;

or assignment are not yet evaluated.
</li>

<li>
If one or more of the communications can proceed,
a single one that can proceed is chosen via a uniform pseudo-random selection.
Otherwise, if there is a default case, that case is chosen.
If there is no default case, the "select" statement blocks until
at least one of the communications can proceed.
</li>

<li>