%arg4: tensor<10xf32> {tf._user_specified_name = "b2"}) -> tensor<10xf32>
          attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<32x28x28x1>, #tf_type.shape<3x3x1x5>, #tf_type.shape<5>, #tf_type.shape<3920x10>, #tf_type.shape<10>]} {
  return %arg4 : tensor<10xf32>
}

// -----

// CHECK-LABEL: strips_attributes_with_tf_values

tensor<?x8x8xf32>, %arg1: tensor<8x10xf32>, %arg2: tensor<8x10xf32>, %arg3: tensor<8x40xf32>, %arg4: tensor<10x40xf32>, %arg5: tensor<40xf32>) -> (tensor<8x10xf32>, tensor<?x8x10xf32>, tensor<8x10xf32>, tensor<8x10xf32>, tensor<f32>) attributes {tf._input_shapes = ["tfshape$dim { size: -1 } dim { size: 8 } dim { size: 8 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$unknown_rank: true", "tfshape$unknown_rank: false", "tfshape$unknown_rank: false"],...

//     if you want shape inference to consider the input tensors of the
//     op for shape inference.
//   - The types need not be set in `input_shapes` as it is not used.
//   - The number of `input_tensors` should be the same as the number of items
//     in `input_shapes`.
//
// The results are returned in `output_shapes` and
// `output_resource_shapes_and_types`. The caller is responsible for freeing the

  // [[g1, g1, g1],
  //  [g2, g2, g2]]

  // input_shape = [2, 3]
  auto input_shape = Shape(scope, op.input(0));

  // output_shape_kept_dims = [2, 1]
  auto output_shape_kept_dims =
      ReducedShapeHelper(scope, input_shape, op.input(1));

  // This step "flips" any 1s with values from the input_shape, and
  // replaces remaining entries with 1. This creates a shape that

    // through entry by entry.
    ArrayRef<int64_t> input_shape = input_type.getShape();
    int input_shape_size = input_shape.size();
    Shape slice_sizes(input_shape.begin(), input_shape.end());
    int slice_dimensions = slice_sizes.size();
    slice_sizes[slice_dimensions - 2] =
        std::min((int64_t)1, input_shape[input_shape_size - 2]);
    slice_sizes[slice_dimensions - 1] =

    }
    func.return %0 : tensor<*xf32>
  }
  func.func private @__inference_call_440(%arg0: tensor<?x28x28x3xf32> {tf._user_specified_name = "inputs"}) -> tensor<*xf32> attributes {tf._input_shapes = [#tf_type.shape<?x28x28x3>], tf.signature.is_stateful} {
    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {value = dense<0.000000e+00> : tensor<32xf32>} : () -> tensor<32xf32>

  // on `xla_call_module_context_` for details.
  std::vector<xla::Shape> input_shapes;
  input_shapes.reserve(op.getArgs().size());
  for (mlir::Type type : op.getArgs().getTypes()) {
    input_shapes.push_back(xla::TypeToShape(type));
  }

  absl::Status status = loader->RefineDynamicShapes(input_shapes);
  if (!status.ok()) {
    // Do not return false here.
    //

        file->getBuffer(), input_arrays, input_dtypes, input_shapes,
        output_arrays, control_output_arrays, graphdef_conversion_options,
        context);
  }
  return GraphdefToMlirTranslateFunction(file->getBuffer(), input_arrays,
                                         input_dtypes, input_shapes,
                                         output_arrays, control_output_arrays,

  int feature_group_cnt = 1;
  ShapedType input_shape =
      mlir::dyn_cast<ShapedType>(op->getOperand(0).getType());
  if (!input_shape) {
    return op->emitError(
        "Only input with known shape is supported for Uniform Quantized "
        "opset.");
  }

  if (op->getParentOfType<func::FuncOp>().getName().contains("depthwise_")) {
    feature_group_cnt = input_shape.getDimSize(3);
  }

  if (input_shapes_attr.size() != func_op.getNumArguments()) {
    return func_op->emitError(
               "Number of arguments and 'tf._input_shapes' "
               "attribute size do not match. ")
           << "Num args: " << func_op.getNumArguments()
           << ", tf._input_shapes size: " << input_shapes_attr.size();
  }

  return success();
}

// Validates ModuleOp. Returns `failure` if the module op is invalid.