# Stubs for tensorflow.contrib.learn.python.learn.estimators.state_saving_rnn_estimator (Python 3)
#
# NOTE: This dynamically typed stub was automatically generated by stubgen.

from tensorflow.contrib import layers as layers
from tensorflow.contrib.layers.python.layers import feature_column_ops as feature_column_ops, optimizers as optimizers
from tensorflow.contrib.learn.python.learn.estimators import constants as constants, estimator as estimator, model_fn as model_fn, rnn_common as rnn_common
from tensorflow.python.framework import dtypes as dtypes, ops as ops, sparse_tensor as sparse_tensor, tensor_shape as tensor_shape
from tensorflow.python.ops import array_ops as array_ops, rnn as rnn
from tensorflow.python.util import nest as nest
from typing import Any as Any, Optional as Optional

def construct_state_saving_rnn(cell: Any, inputs: Any, num_label_columns: Any, state_saver: Any, state_name: Any, scope: str = ...): ...
def state_tuple_to_dict(state: Any): ...

class StateSavingRnnEstimator(estimator.Estimator):
    def __init__(self, problem_type: Any, num_unroll: Any, batch_size: Any, sequence_feature_columns: Any, context_feature_columns: Optional[Any] = ..., num_classes: Optional[Any] = ..., num_units: Optional[Any] = ..., cell_type: str = ..., optimizer_type: str = ..., learning_rate: float = ..., predict_probabilities: bool = ..., momentum: Optional[Any] = ..., gradient_clipping_norm: float = ..., dropout_keep_probabilities: Optional[Any] = ..., model_dir: Optional[Any] = ..., config: Optional[Any] = ..., feature_engineering_fn: Optional[Any] = ..., num_threads: int = ..., queue_capacity: int = ..., seed: Optional[Any] = ...) -> None: ...

0b660c8f31220069c5b63b964760440fea868b5d
<mask token>


class StateSavingRnnEstimator(estimator.Estimator):

    def __init__(self, problem_type: Any, num_unroll: Any, batch_size: Any,
        sequence_feature_columns: Any, context_feature_columns: Optional[
        Any]=..., num_classes: Optional[Any]=..., num_units: Optional[Any]=
        ..., cell_type: str=..., optimizer_type: str=..., learning_rate:
        float=..., predict_probabilities: bool=..., momentum: Optional[Any]
        =..., gradient_clipping_norm: float=..., dropout_keep_probabilities:
        Optional[Any]=..., model_dir: Optional[Any]=..., config: Optional[
        Any]=..., feature_engineering_fn: Optional[Any]=..., num_threads:
        int=..., queue_capacity: int=..., seed: Optional[Any]=...) ->None:
        ...

<mask token>


def state_tuple_to_dict(state: Any):
    ...


class StateSavingRnnEstimator(estimator.Estimator):

    def __init__(self, problem_type: Any, num_unroll: Any, batch_size: Any,
        sequence_feature_columns: Any, context_feature_columns: Optional[
        Any]=..., num_classes: Optional[Any]=..., num_units: Optional[Any]=
        ..., cell_type: str=..., optimizer_type: str=..., learning_rate:
        float=..., predict_probabilities: bool=..., momentum: Optional[Any]
        =..., gradient_clipping_norm: float=..., dropout_keep_probabilities:
        Optional[Any]=..., model_dir: Optional[Any]=..., config: Optional[
        Any]=..., feature_engineering_fn: Optional[Any]=..., num_threads:
        int=..., queue_capacity: int=..., seed: Optional[Any]=...) ->None:
        ...

<mask token>


def construct_state_saving_rnn(cell: Any, inputs: Any, num_label_columns:
    Any, state_saver: Any, state_name: Any, scope: str=...):
    ...


def state_tuple_to_dict(state: Any):
    ...


class StateSavingRnnEstimator(estimator.Estimator):

    def __init__(self, problem_type: Any, num_unroll: Any, batch_size: Any,
        sequence_feature_columns: Any, context_feature_columns: Optional[
        Any]=..., num_classes: Optional[Any]=..., num_units: Optional[Any]=
        ..., cell_type: str=..., optimizer_type: str=..., learning_rate:
        float=..., predict_probabilities: bool=..., momentum: Optional[Any]
        =..., gradient_clipping_norm: float=..., dropout_keep_probabilities:
        Optional[Any]=..., model_dir: Optional[Any]=..., config: Optional[
        Any]=..., feature_engineering_fn: Optional[Any]=..., num_threads:
        int=..., queue_capacity: int=..., seed: Optional[Any]=...) ->None:
        ...

from tensorflow.contrib import layers as layers
from tensorflow.contrib.layers.python.layers import feature_column_ops as feature_column_ops, optimizers as optimizers
from tensorflow.contrib.learn.python.learn.estimators import constants as constants, estimator as estimator, model_fn as model_fn, rnn_common as rnn_common
from tensorflow.python.framework import dtypes as dtypes, ops as ops, sparse_tensor as sparse_tensor, tensor_shape as tensor_shape
from tensorflow.python.ops import array_ops as array_ops, rnn as rnn
from tensorflow.python.util import nest as nest
from typing import Any as Any, Optional as Optional


def construct_state_saving_rnn(cell: Any, inputs: Any, num_label_columns:
    Any, state_saver: Any, state_name: Any, scope: str=...):
    ...


def state_tuple_to_dict(state: Any):
    ...


class StateSavingRnnEstimator(estimator.Estimator):

    def __init__(self, problem_type: Any, num_unroll: Any, batch_size: Any,
        sequence_feature_columns: Any, context_feature_columns: Optional[
        Any]=..., num_classes: Optional[Any]=..., num_units: Optional[Any]=
        ..., cell_type: str=..., optimizer_type: str=..., learning_rate:
        float=..., predict_probabilities: bool=..., momentum: Optional[Any]
        =..., gradient_clipping_norm: float=..., dropout_keep_probabilities:
        Optional[Any]=..., model_dir: Optional[Any]=..., config: Optional[
        Any]=..., feature_engineering_fn: Optional[Any]=..., num_threads:
        int=..., queue_capacity: int=..., seed: Optional[Any]=...) ->None:
        ...

# Stubs for tensorflow.contrib.learn.python.learn.estimators.state_saving_rnn_estimator (Python 3)
#
# NOTE: This dynamically typed stub was automatically generated by stubgen.

from tensorflow.contrib import layers as layers
from tensorflow.contrib.layers.python.layers import feature_column_ops as feature_column_ops, optimizers as optimizers
from tensorflow.contrib.learn.python.learn.estimators import constants as constants, estimator as estimator, model_fn as model_fn, rnn_common as rnn_common
from tensorflow.python.framework import dtypes as dtypes, ops as ops, sparse_tensor as sparse_tensor, tensor_shape as tensor_shape
from tensorflow.python.ops import array_ops as array_ops, rnn as rnn
from tensorflow.python.util import nest as nest
from typing import Any as Any, Optional as Optional

def construct_state_saving_rnn(cell: Any, inputs: Any, num_label_columns: Any, state_saver: Any, state_name: Any, scope: str = ...): ...
def state_tuple_to_dict(state: Any): ...

class StateSavingRnnEstimator(estimator.Estimator):
    def __init__(self, problem_type: Any, num_unroll: Any, batch_size: Any, sequence_feature_columns: Any, context_feature_columns: Optional[Any] = ..., num_classes: Optional[Any] = ..., num_units: Optional[Any] = ..., cell_type: str = ..., optimizer_type: str = ..., learning_rate: float = ..., predict_probabilities: bool = ..., momentum: Optional[Any] = ..., gradient_clipping_norm: float = ..., dropout_keep_probabilities: Optional[Any] = ..., model_dir: Optional[Any] = ..., config: Optional[Any] = ..., feature_engineering_fn: Optional[Any] = ..., num_threads: int = ..., queue_capacity: int = ..., seed: Optional[Any] = ...) -> None: ...
