}
  }
)mlir";

constexpr absl::string_view kModuleDynamic = R"mlir(
  module {
    func.func @main(%arg0: tensor<?x1024xf32>, %arg1: tensor<1024x3xf32>) -> tensor<?x3xf32> attributes {_from_xla_call_module} {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0], precision = [] : (tensor<?x1024xf32>, tensor<1024x3xf32>) -> tensor<?x3xf32>
      return %0 : tensor<?x3xf32>
    }
  }
)mlir";

func.func @multinomial_i32(%arg0: tensor<2xf32>, %arg1: tensor<1xi32>) -> tensor<10xi32> {
  %0 = "tf.Multinomial"(%arg0, %arg1) {seed = 0 : i64, seed2 = 0: i64} : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi32>
  func.return %0 : tensor<10xi32>

// CHECK-LABEL:multinomial_i32
// CHECK: "tfl.multinomial"(%arg0, %arg1) <{seed = 0 : i64, seed2 = 0 : i64}> : (tensor<2xf32>, tensor<1xi32>) -> tensor<10xi32>
}

  %0 = "tfl.pseudo_qconst"() {qtype = tensor<128x!quant.uniform<i32:f32, 0.7>>, value = dense<0> : tensor<128xi32>} : () -> tensor<128x!quant.uniform<i32:f32, 0.7>>

  func.func @random_uniform_int(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<1000xi32> {
    %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
    // CHECK-NOT: tf.RandomUniformInt
    %1 = "tf.RandomUniformInt"(%0, %arg0, %arg1) {seed = 0 : i64, seed2 = 0 : i64} : (tensor<1xi32>, tensor<i32>, tensor<i32>) -> tensor<1000xi32>
    func.return %1 : tensor<1000xi32>
  }

  // CHECK-LABEL: multinomial

func.func @reshape_vector_shape(tensor<4x4x4xf32>) -> tensor<16x4xf32> {
^bb0(%arg0: tensor<4x4x4xf32>) :
  %shape0 = arith.constant dense<[[16, 4]]> : tensor<1x2xi32>
  // expected-error @+1 {{'tfl.reshape' op requires 'shape' to be rank 1, but got 2}}
  %1 = "tfl.reshape"(%arg0, %shape0) : (tensor<4x4x4xf32>, tensor<1x2xi32>) -> tensor<16x4xf32>
  func.return %1 : tensor<16x4xf32>
}

// -----

// CHECK-DAG: %[[SCALE:.*]] = "tf.Const"() <{value = dense<0.0787401571> : tensor<f32>
}

// -----

module {
  func.func @quantize_xlagather(%arg0: tensor<10x2xi32>) -> tensor<1x300x10xf32> {
    %cst_0 = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<200x100x300xf32>} : () -> tensor<200x100x300xf32>

func.func @testTranspose(tensor<2x3xf32>, tensor<1x2xi32>) -> tensor<3x2xf32> {
^bb0(%arg0: tensor<2x3xf32>, %arg1: tensor<1x2xi32>):
  // expected-error @+1 {{expected perm to be a 1-D Tensor, got perm of rank 2}}
  %0 = "tf.Transpose"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", Tperm = "tfdtype$DT_INT32"} : (tensor<2x3xf32>, tensor<1x2xi32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>
}

// -----

  %0 = stablehlo.constant() {value = dense<1> : tensor<1024x3xi8>} : () -> tensor<1024x3x!quant.uniform<i8<-127:127>:f32:1, {2.000000e+0, 2.000000e+0, 2.000000e+0}>>

func.func @testGatherToV2(%params: tensor<4x3xf32>, %indices: tensor<1x2xi32>) -> tensor<2x3xf32> {
  // CHECK: %[[AXIS:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
  // CHECK: "tf.GatherV2"(%arg0, %arg1, %[[AXIS]]) <{batch_dims = 0 : i64}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<4x3xf32>, tensor<1x2xi32>, tensor<i32>) -> tensor<2x3xf32>

  url: https://github.com/JarroVGIT
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