message: Simplify graphdef2mlir/prune_unused_nodes.pbtxt test to be more targeted
  This test intends to check the pruning behavior, the CHECK lines are updated to
  CHECK the absence of the pruned node in the output instead of positively checking
  everything else.
diff: # RUN: tf-mlir-translate -graphdef-to-mlir %s -tf-prune-unused-nodes -tf-input-arrays=input0,input1,unused_input -tf-input-data-types=DT_INT32,DT_INT32,DT_INT32 -tf-input-shapes=10:10:10 -tf-output-arrays=Add -o - | FileCheck %s
  +# Verify that an unused Node (here named "Prune") isn't converted when we
  +# request pruning on import.
  +# CHECK-LABEL: func @main
  +# CHECK-NOT: Prune
  +
  node {
  name: "Prune"
  op: "Const"
  @@ -66,13 +71,3 @@ node {
  versions {
  producer: 27
  }
  -
  -# CHECK: func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>, %arg2: tensor<10xi32>) -> tensor<10xi32>
  -# CHECK-NEXT: attributes {tf.entry_function = {inputs = "input0, input1, unused_input", outputs = "Add"}} {
  -# CHECK-NEXT: %0:2 = "_tf.Placeholder.input"(%arg0) {device = "", dtype = "tfdtype$DT_INT32", name = "input0", shape = "tfshape$dim { size: 10 }"} : (tensor<10xi32>) -> (tensor<10xi32>, !_tf.control)
  -# CHECK-NEXT: %1:2 = "_tf.Placeholder.input"(%arg1) {device = "", dtype = "tfdtype$DT_INT32", name = "input1", shape = "tfshape$dim { size: 10 }"} : (tensor<10xi32>) -> (tensor<10xi32>, !_tf.control)
  -# CHECK-NEXT: %2:2 = "_tf.Add"(%0#0, %1#0) {T = "tfdtype$DT_INT32", device = "", name = "Add"} : (tensor<10xi32>, tensor<10xi32>) -> (tensor<10xi32>, !_tf.control)
  -# CHECK-NEXT: %3:2 = "_tf.Placeholder.input"(%arg2) {device = "", dtype = "tfdtype$DT_INT32", name = "unused_input", shape = "tfshape$dim { size: 10 }"} : (tensor<10xi32>) -> (tensor<10xi32>, !_tf.control)
  -# CHECK-NEXT: return %2#0 : tensor<10xi32>
  -# CHECK-NEXT: }
  -
  