func.return %0 : tensor<3x4xi1>
}

// CHECK-LABEL: @is_nan
func.func @is_nan(%arg0: tensor<3x4xf32>) -> tensor<3x4xi1> {
  // CHECK: %[[RESULT:.*]] = "tf.NotEqual"(%arg0, %arg0) <{incompatible_shape_error = true}> : (tensor<3x4xf32>, tensor<3x4xf32>) -> tensor<3x4xi1>
  %0 = "tf.IsNan"(%arg0) : (tensor<3x4xf32>) -> tensor<3x4xi1>
  // CHECK: return %[[RESULT]]
  func.return %0 : tensor<3x4xi1>
}

    %9 = "tf.Identity"(%cst_2) {device = ""} : (tensor<3x4xi8>) -> tensor<3x4xi8>
    %10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<3x4xi8>) -> tensor<3x4xi32>
    %11 = "tf.Einsum"(%8, %10) {device = "", equation = "ab,bc->ac"} : (tensor<2x3xi32>, tensor<3x4xi32>) -> tensor<2x4xi32>
    %12 = "tf.Cast"(%11) {Truncate = false, device = ""} : (tensor<2x4xi32>) -> tensor<2x4xf32>

  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

// CHECK: %[[UNIFORM_QUANTIZE:.+]] = stablehlo.uniform_quantize %[[ARG_0]] : (tensor<3x4x2xf32>) -> tensor<3x4x2x!quant.uniform<i8:f32, {{.*}}>>
// CHECK: %[[CALL:.+]] = call @quantized_gather_fn(%[[UNIFORM_QUANTIZE]], %[[CONST]]) {_quantization_method = "static_range_ptq { }"} : (tensor<3x4x2x!quant.uniform<i8:f32, {{.*}}>>, tensor<2x3x2xi32>) -> tensor<2x3x2x2x!quant.uniform<i8:f32, {{.*}}>>

// TF-DAG: %[[cc_0:.*]] = "tf.ConcatV2"(%[[pc_1]], %[[pc_5]], %[[cst_0]]) : (tensor<2x2xi8>, tensor<2x2xi8>, tensor<i32>) -> tensor<2x4xi8>
// TF-DAG: %[[pc_7:.*]] = "tf.PartitionedCall"(%[[cc_0]], %[[cst_6]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @dequantize_i8}
// TF-DAG: return %[[pc_7]]