llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> unconditionally_use_set_output_shapes(
    "tf-enable-unconditionally-use-set-output-shapes-on-import",
    llvm::cl::desc("Enable using the _output_shapes unconditionally on import "
                   "(temporary)"),
    llvm::cl::init(false));

// NOLINTNEXTLINE
opt<bool> enable_soft_placement(
    "tf-enable-soft-placement-on-import",

    value {
      i: 2
    }
  }
}
node {
  name: "args_3"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 2
          }
          dim {
            size: 4
          }
          dim {
            size: 6

  bool restrict_functionalization_to_compiled_nodes = false;
  // If true, enables shape inference on input.
  // TODO(jpienaar): This will be removed shortly.
  bool enable_shape_inference = true;
  // _output_shapes is an unregistered attribute which is used during
  // GraphConstructor::ConvertGraph to override shapes. It is unfortunately
  // not always set correctly (which is undesirable and should be addressed)

  // Create a bad output shape attr.
  AttrValue shape_attr;
  TensorShapeProto* shape_proto = shape_attr.mutable_list()->add_shape();
  shape_proto->add_dim()->set_size(1);
  builder.Attr("_output_shapes", shape_attr);

  TF_RETURN_IF_ERROR(builder.Finalize(&node));

  return CreateSingleOpGraph(node, {}, {DataType::DT_INT32});
}

absl::Status BuildHloFromGraph(Graph& graph, bool use_output_shapes) {

// HLO-level inputs are supplied, and HLO-level outputs are produced.
// xla_params is the HLO-level inputs and returns is the HLO-level outputs.
// If unconditionally_use_output_shapes is true then the unregistered
// attribute _output_shapes is always used to set the output shapes of the ops.
ABSL_DEPRECATED(
    "Use v1/compile_tf_graph.h::CompileTensorflowGraphToHlo instead.")
Status BuildHloFromGraph(
    const Graph& graph, xla::XlaBuilder& builder,

            }
          }
          attr {
            key: "_output_shapes"
            value {
              list {
                shape {
                }
              }
            }
          }
        }
        node_def {
          name: "num_shards"
          op: "Const"
          attr {
            key: "_output_shapes"
            value {
              list {
                shape {

  %cst_0 = arith.constant dense<1> : tensor<3xi32>
  %0 = "tf.Squeeze"(%arg0) {T = f32, _output_shapes = ["tfshape$dim { size: 4 } dim { size: 64 } dim { size: 64 }"], device = "", squeeze_dims = []} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>

                    )
                ]
            )
        )
        self.assertTrue(
            self._contains_op(
                output_graphdef,
                'Const',
                '_output_shapes',
                per_channel_size_attr,
            )
        )
    elif target_opset == quant_opts_pb2.UNIFORM_QUANTIZED:
      self.assertTrue(

  if (node.IsWhileNode()) {
    auto* output_shapes = node.attrs().Find("output_shapes");
    auto* element_types = node.attrs().Find("T");
    if (output_shapes && !output_shapes->list().shape().empty()) {
      const auto& output_shape = output_shapes->list().shape(idx);
      const auto& element_type = element_types->list().type(idx);
      return ConvertToMlirTensorType(output_shape, element_type, &builder);
    }
  }

      (*node_def->mutable_attr())["_handle_dtypes"] = handle_dtypes_attr;
      (*node_def->mutable_attr())["_handle_shapes"] = handle_shapes_attr;
    }
  }

  TF_RETURN_IF_ERROR(
      SetShapeAttribute("_output_shapes", arg_type, node_def->mutable_attr()));

  DataType dtype;
  TF_RETURN_IF_ERROR(ConvertToDataType(arg_type.getElementType(), &dtype));
  AttrValue type_attr;
  type_attr.set_type(dtype);