'activation_fn': nn_ops.relu,
          'has_bias': True,
      },
  )
  def test_conv2d_ptq_model_whole_model_verify(self, activation_fn, has_bias):
    input_shape = [None, None, None, 3]
    filter_shape = [2, 3, 3, 2]

    model = self._create_conv2d_model(
        input_shape,
        filter_shape,
        activation_fn=activation_fn,
        has_bias=has_bias,
    )

      # If present the last op before return should be stablehlo.clamp for relu6
      # and stablehlo.maximum for relu.
      if activation_fn is nn_ops.relu6:
        self.assertRegex(module_str, r'stablehlo.clamp.*\n.*return')
      elif activation_fn is nn_ops.relu:
        self.assertRegex(module_str, r'stablehlo.maximum.*\n.*return')
    else:
      # Check activation functions are implicit.

        """
        out = math_ops.matmul(input_tensor, self.filters, name='sample/matmul')
        if bias_fn is not None:
          out = bias_fn(out, self.bias)
        if activation_fn is not None:
          out = activation_fn(out)
        return {'output': out}

    model = MatmulModel(weight_shape)
    saved_model_save.save(
        model,
        saved_model_path,

          )
        if activation_fn is not None:
          out = activation_fn(out)
        return {'output': out}

    return DepthwiseConvModel()

  def _create_conv2d_model(
      self,
      input_shape: Sequence[int],
      filter_shape: Sequence[int],
      has_bias: bool = False,
      has_batch_norm: bool = False,
      activation_fn: Optional[ops.Operation] = None,

    key: "use_nesterov"
    value {
      b: false
    }
  }
}
node {
  name: "training/SGD/gradients/activation_50/Relu_grad/ReluGrad"
  op: "ReluGrad"
  input: "training/SGD/gradients/max_pooling2d_1/MaxPool_grad/MaxPoolGrad"
  input: "activation_50/Relu"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT

    value {
      b: false
    }
  }
}
node {
  name: "training/LossScaleOptimizer/gradients/activation_50/Relu_grad/ReluGrad"
  op: "ReluGrad"
  input: "training/LossScaleOptimizer/gradients/pool1_pad_1/Pad_grad/Slice_1"
  input: "activation_50/Relu"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {
      type: DT_HALF