// CHECK-SAME: (tensor<2xi32>) -> tensor<1xi32>
    // CHECK: %[[DIM0:.*]] = mhlo.reshape %[[SLICE0]] : (tensor<1xi32>) -> tensor<i32>

    // CHECK: %[[SLICE1:.*]] = "mhlo.slice"(%[[ARG2]])
    // CHECK-DAG-SAME: start_indices = dense<1> : tensor<1xi64>
    // CHECK-DAG-SAME: limit_indices = dense<2> : tensor<1xi64>
    // CHECK-DAG-SAME: strides = dense<1> : tensor<1xi64>

  %3 = "tf.AddV2"(%2, %1): (tensor<15x28x28x1xf32>, tensor<1xf32>) -> tensor<15x28x28x1xf32>
  func.return %2 : tensor<15x28x28x1xf32>

}

// CHECK: %[[maxpool_i8:.*]] = "tf.MaxPool"
// CHECK-SAME: (tensor<*xi8>) -> tensor<*xi8>
// CHECK: %[[reshape_i8:.*]] = "tf.Reshape"(%[[maxpool_i8]]
// CHECK-SAME: (tensor<*xi8>, tensor<2xi32>) -> tensor<*xi8>
// CHECK: %[[scast:.*]] = "quantfork.scast"(%[[reshape_i8]]
// CHECK: %[[matmul:.*]] = "tf.PartitionedCall"(%[[scast]]
// CHECK-SAME: f = @composite_matmul_fn_1

// a named type remain the same as long as the same source and AddMethod calls
// are presented to the type checker in the same order (go.dev/issue/61298).
func TestMethodOrdering(t *testing.T) {
	const src = `
package p

type T struct{}

func (T) a() {}
func (T) c() {}
func (T) b() {}
`
	// should get the same method order each time
	var methods []string

            // Redirect stderr to stdout when both stdout and stderr are attached to a console. Assume that they are attached to the same console
            // This avoids interleaving problems when stdout and stderr end up at the same location
            Console console = consoleFor(stdout, consoleMetaData, renderer.getColourMap());

// MergeGateways combines multiple gateways targeting the same workload into a single logical Gateway.
// Note that today any Servers in the combined gateways listening on the same port must have the same protocol.
// If servers with different protocols attempt to listen on the same port, one of the protocols will be chosen at random.

// CHECK:             tfl.yield
// CHECK-SAME:  (tensor<i1>) -> ()
// CHECK:             [[VAL_30:%.*]]:7 =
// CHECK: call @tfl.while_body
// CHECK:             tfl.yield
// CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<*xf32>, tensor<4x2xf32>, tensor<4x2xf32>, tensor<*xf32>, tensor<4x4x3xf32>) -> ()

// CHECK-LABEL:   func private @tfl.while_cond(

// CHECK-SAME: effective_hidden_scale_intermediate = tensor<*x!quant.calibrated<f32<-5.000000e-01:5.000000e-01>>>
// CHECK-SAME: input_to_cell_intermediate = tensor<*x!quant.calibrated<f32<-4.000000e+00:4.000000e+00>>>
// CHECK-SAME: input_to_forget_intermediate = tensor<*x!quant.calibrated<f32<-1.600000e+01:1.600000e+01>>>

class OperandsSameElementTypeConstraintBase<string op> :
  PredOpTrait<op # " operands have same element type",
    OperandsSameElementTypeConstraintBasePred>;

// This is a constraint for most of the binary ops, e.g., add, mul, div, etc.
// Binary ops lhs & rhs should have the same value type, and is capable to
// compare quantization types as well.
def BinaryOpSameElementTypeConstraint :

  %3 = "tf.AddV2"(%2, %1): (tensor<15x28x28x1xf32>, tensor<1xf32>) -> tensor<15x28x28x1xf32>
  func.return %2 : tensor<15x28x28x1xf32>