%11 = stablehlo.reshape %2 : (tensor<1x1x1x1xi8>) -> tensor<1xi8>
    %12 = stablehlo.broadcast_in_dim %11, dims = [0] : (tensor<1xi8>) -> tensor<1x3x3x4xi8>
    %13 = stablehlo.convert %12 : (tensor<1x3x3x4xi8>) -> tensor<1x3x3x4xf32>
    %14 = stablehlo.convert %3 : (tensor<3x3x4x4xi8>) -> tensor<3x3x4x4xf32>

func.func @testLogicalAndDoesntFoldWithConstantFalseBroadcast(%arg0: tensor<2xi1>) -> (tensor<2xi1>) {
  %cst = arith.constant dense<false> : tensor<i1>

  // CHECK: %[[LOGICAL_AND:.*]] = "tf.LogicalAnd"
  %0 = "tf.LogicalAnd"(%cst, %arg0) : (tensor<i1>, tensor<2xi1>) -> tensor<2xi1>

  // CHECK: return %[[LOGICAL_AND]]
  func.return %0: tensor<2xi1>
}

// -----

// GlobalIterId should not be constant folded

  }

  // CHECK-LABEL: func @greater
  func.func @greater(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2xi1> {
    // CHECK-NEXT:  mhlo.compare GT, %arg0, %arg1
    %0 = "tf.Greater"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi1>
    func.return %0: tensor<2xi1>
  }

  // CHECK-LABEL: batchmatmulv2

// CHECK-LABEL: fused_conv2d
func.func @fused_conv2d(%input: tensor<1x300x300x40xi8>,
                        %filter: tensor<3x3x40x40xi8>,
                        %bias: tensor<40xf32>,
                        %act: tensor<0xi8>) -> tensor<1x300x300x40xi8> {

  // CHECK:       %[[v0:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
  // CHECK-NEXT:  %[[v1:.*]] = mhlo.constant dense<2.000000e+00> : tensor<f32>

    %cst_7 = "tfl.pseudo_qconst"() {qtype = tensor<20x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20xi8>} : () -> tensor<20x!quant.uniform<i8:f32, 0.006:-34>>
    %cst_11 = "tfl.pseudo_qconst"() {qtype = tensor<20x28x!quant.uniform<i8:f32, 0.006:-34>>, value = dense<1> : tensor<20x28xi8>} : () -> tensor<20x28x!quant.uniform<i8:f32, 0.006:-34>>

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