message: We no longer need updates_collections in quant ops since we rely on the data dependency from Assign ops.
diff: @@ -51,7 +51,6 @@ def LastValueQuantize(inputs,
  per_channel=False,
  init_min=-6.0,
  init_max=6.0,
  - updates_collection=ops.GraphKeys.UPDATE_OPS,
  vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
  name_prefix='LastValueQuant',
  reuse=None,
  @@ -69,8 +68,6 @@ def LastValueQuantize(inputs,
  quantization ranges per output channel.
  init_min: a float scalar, the initial value for variable min.
  init_max: a float scalar, the initial value for variable max.
  - updates_collection: (Optional) collections to collect the update ops for
  - computation.
  vars_collection: (Optional) collection where to store variables for
  quantization interval ends.
  name_prefix: name_prefix for created nodes.
  @@ -133,7 +130,6 @@ def LastValueQuantize(inputs,
  # TFLite requires that 0.0 if always in the [min; max] range.
  batch_min = math_ops.minimum(batch_min, 0.0)
  assign_min = state_ops.assign(min_var, batch_min, name='AssignMinLast')
  - ops.add_to_collection(updates_collection, assign_min.op)
  if per_channel:
  if input_dim >= 2:
  @@ -146,7 +142,6 @@ def LastValueQuantize(inputs,
  # TFLite requires that 0.0 if always in the [min; max] range.
  batch_max = math_ops.maximum(batch_max, 0.0)
  assign_max = state_ops.assign(max_var, batch_max, name='AssignMaxLast')
  - ops.add_to_collection(updates_collection, assign_max.op)
  return _FakeQuantWithMinMaxVars(
  inputs,
  @@ -163,7 +158,6 @@ def MovingAvgQuantize(inputs,
  init_min=-6.0,
  init_max=6.0,
  ema_decay=0.999,
  - updates_collection=ops.GraphKeys.UPDATE_OPS,
  vars_collection=ops.GraphKeys.MOVING_AVERAGE_VARIABLES,
  name_prefix='MovingAvgQuantize',
  reuse=None,
  @@ -182,8 +176,6 @@ def MovingAvgQuantize(inputs,
  init_min: a float scalar, the initial value for variable min.
  init_max: a float scalar, the initial value for variable max.
  ema_decay: EMA decay parameter.
  - updates_collection: (Optional) collections to collect the update ops for
  - computation.
  vars_collection: (Optional) collection where to store variables for
  quantization interval ends.
  name_prefix: name_prefix for created nodes.
  @@ -246,7 +238,6 @@ def MovingAvgQuantize(inputs,
  batch_min = math_ops.minimum(batch_min, 0.0)
  assign_min = moving_averages.assign_moving_average(
  min_var, batch_min, ema_decay, name='AssignMinEma')
  - ops.add_to_collection(updates_collection, assign_min.op)
  if per_channel:
  if input_dim >= 2:
  @@ -260,7 +251,6 @@ def MovingAvgQuantize(inputs,
  batch_max = math_ops.maximum(batch_max, 0.0)
  assign_max = moving_averages.assign_moving_average(
  max_var, batch_max, ema_decay, name='AssignMaxEma')
  - ops.add_to_collection(updates_collection, assign_max.op)
  return _FakeQuantWithMinMaxVars(
  inputs,
  