}];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same per axis uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_vars_per_channel op. See the fakeQuantAttrsToType()
    utility method and the quant-convert-simulated-quantization pass for further
    details.
  }];

  let arguments = (ins
    F32Tensor:$inputs,

  }];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same per axis uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_vars_per_channel op. See the fakeQuantAttrsToType()
    utility method and the quant-convert-simulated-quantization pass for further
    details.
  }];

  let arguments = (ins
    F32Tensor:$inputs,

        )
        filter_max = array_ops.identity(
            array_ops.constant([0.5, 0.5], dtype=dtypes.float32)
        )
        q_filter = array_ops.fake_quant_with_min_max_vars_per_channel(
            filter_tensor, filter_min, filter_max, num_bits=8, narrow_range=True
        )
        bias = array_ops.constant([0.1, 0.2], dtype=dtypes.float32)
        scale, offset = [1.0] * 2, [0.5] * 2

        `tf.quantization.fake_quant_with_min_max_args_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars`,
        `tf.quantization.fake_quant_with_min_max_vars_gradient`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel`,
        `tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient`.
    *   New endpoints in tf.strings namespace: `tf.strings.join` (corresponds to