{
          "shape": [1, 4],
          "name": "input_activation_state18",
          "is_variable": true,
          "quantization": {
            "min": [-0.8],
            "max": [1.6]
          }
        },
        {
          "shape": [1, 2],
          "name": "input_cell_state19",
          "is_variable": true,
          "quantization": {
            "min": [-2.0],
            "max": [4.0]

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main() -> tensor<3x2xi32> {
  %0 = "tfl.pseudo_const" () {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>
}

// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ ],
// CHECK-NEXT:     subgraphs: [ {
// CHECK-NEXT:       tensors: [ {

// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         name: "Const",
// CHECK-NEXT:         quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:         },
// CHECK-NEXT:         is_variable: true,
// CHECK-NEXT:         has_rank: true
// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 6,
// CHECK-NEXT:         name: "tfl.svdf",

// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         name: "Const",
// CHECK-NEXT:         quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:         },
// CHECK-NEXT:         is_variable: true,
// CHECK-NEXT:         has_rank: true
// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 6,
// CHECK-NEXT:         name: "tfl.svdf",

// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4, 4 ],
// CHECK-NEXT:         name: "Const",
// CHECK-NEXT:         quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:         },
// CHECK-NEXT:         is_variable: true,
// CHECK-NEXT:         has_rank: true
// CHECK-NEXT:       }, {
// CHECK-NEXT:         shape: [ 4 ],
// CHECK-NEXT:         buffer: 6,
// CHECK-NEXT:         name: "tfl.unidirectional_sequence_rnn",

internal class JavaElementDelegatingVariableReturnTypeSourceWithSmartPointer<TYPE : PsiType>(
    override val psiPointer: SmartPsiElementPointer<out PsiVariable>,
    override val factory: JavaElementSourceFactory,
) : JavaElementDelegatingTypeSourceWithSmartPointer<PsiVariable, TYPE>() {

    override fun getType(psi: PsiVariable): TYPE {
        @Suppress("UNCHECKED_CAST")
        return psi.type as TYPE
    }
}

 */

package org.jetbrains.kotlin.analysis.api.impl.base.test.cases.components.psiTypeProvider

import com.intellij.psi.PsiElement
import com.intellij.psi.PsiMethod
import com.intellij.psi.PsiType
import com.intellij.psi.PsiVariable
import org.jetbrains.kotlin.analysis.api.types.KaType
import org.jetbrains.kotlin.analysis.test.framework.base.AbstractAnalysisApiBasedTest
import org.jetbrains.kotlin.analysis.test.framework.project.structure.KtTestModule

class Child(tf.Module):

  def __init__(self):
    super(Child, self).__init__()
    self.my_variable = tf.Variable(3.)


# Creates a dag object graph.
# There is only one instance of `Child`, but it is reachable via two names.
# Thus, self.my_variable is reachable via two paths.
class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()

  def __init__(self, parent):
    super(ReferencesParent, self).__init__()
    self.parent = parent
    # CHECK: tf_saved_model.global_tensor
    # CHECK-SAME: tf_saved_model.exported_names = ["child.my_variable"]
    self.my_variable = tf.Variable(3.)


# Creates a cyclic object graph.
class TestModule(tf.Module):

  def __init__(self):
    super(TestModule, self).__init__()
    self.child = ReferencesParent(self)