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# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
# pylint: disable=import-outside-toplevel
"""TF: Tensorflow frontend."""
import warnings
from collections import defaultdict

# Numpy support
import numpy as np
import tvm

from tvm.ir import IRModule
from tvm.relay.prelude import Prelude
from tvm.ir import structural_hash as s_hash

from .. import analysis
from .. import expr as _expr
from .. import function as _function
from .. import op as _op
from ..expr_functor import ExprMutator, ExprVisitor
from .common import AttrCvt, get_relay_op
from .common import infer_type as _infer_type
from .common import infer_shape as _infer_shape
from .common import infer_channels as _infer_channels
from .common import infer_value as _infer_value
from .common import infer_value_simulated as _infer_value_simulated

__all__ = ['from_tensorflow']


def _get_pad_pair(input1d, kernel1d, stride1d):
    if input1d % stride1d == 0:
        pad = max(kernel1d - stride1d, 0)
    else:
        pad = max(kernel1d - (input1d % stride1d), 0)

    pad_before = pad // 2
    pad_after = pad - pad_before

    return [pad_before, pad_after]

def _math_name_picker(surfix):
    def _impl(attr):
        return 'broadcast_' + surfix
    return _impl

def _dimension_picker(prefix, surfix=''):
    def _impl(attr):
        kernel = attr['kernel_shape']
        if len(kernel) == 2:
            return prefix + '2d' + surfix
        if len(kernel) == 3:
            return prefix + '3d' + surfix
        raise tvm.error.OpAttributeInvalid(
            'Only 2D or 3D kernels are supported for operator {}'.format(prefix + '2d or 3d'))
    return _impl

def _dimension_constraint():
    def _dim_check(attrs):
        if len(attrs['kernel_shape']) in (2, 3):
            return True
        return False
    return _dim_check, "Only 2d or 3d kernel supported."

def _get_param(params, input_node):
    if isinstance(input_node, _expr.Constant):
        return np.atleast_1d(input_node.data.asnumpy())
    return params[input_node.name_hint].asnumpy()

def _get_num_param(params, input_node):
    return _get_param(params, input_node).item()

def _get_list_param(params, input_node):
    return _get_param(params, input_node).tolist()

def _get_tuple_param(params, input_node):
    return tuple(_get_param(params, input_node))

def _need_prelude_for_shape_inference(op):
    return "TensorArray" in op

def _rsqrt():
    def _impl(inputs, attr, params, mod):
        inputs.append(tvm.relay.const(-0.5, attr['T'].name))
        return AttrCvt(op_name="power")(inputs, attr)
    return _impl

def _argx(func, func_name):
    """ A common wrapper for argmin and argmax operations """
    def _impl(inputs, attr, params, mod):
        try:
            # In Tensorflow, `axis` argument is a Tensor, not attribute. We
            # support the case where it inputs from a scalar constant.
            axis_input_value = [_get_num_param(params, inputs[1])]
        except (IndexError, KeyError):
            raise TypeError(
                "Unsupported argument for `{}` : `axis` should be a constant".format(func_name))
        return func(inputs[0], axis=axis_input_value, keepdims=False)
    return _impl

def _elemwise(name):
    def _impl(inputs, attr, params, mod):
        assert len(inputs) == 2, "{} take 2 inputs, {} given".format(name, len(inputs))
        return get_relay_op(name)(*inputs)
    return _impl

def _pool3d(name):
    def _impl(inputs, attr, params, mod):
        attr['data_format'] = attr['data_format'].decode("utf-8")
        flip_layout = False

        input_shape = _infer_shape(inputs[0], mod)

        if attr['data_format'] == 'NDHWC':
            attr['kernel_shape'] = (attr['ksize'][1], attr['ksize'][2], attr['ksize'][3])
            attr['strides'] = (attr['strides'][1], attr['strides'][2], attr['strides'][3])
        elif attr['data_format'] == 'NCDHW':
            attr['kernel_shape'] = (attr['ksize'][2], attr['ksize'][3], attr['ksize'][4])
            attr['strides'] = (attr['strides'][2], attr['strides'][3], attr['strides'][4])
        else:
            msg = 'Value {} of attribute "data_format" of operator Pooling ' \
                  'is not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))
        if attr['data_format'] == "NDHWC":
            input_shape = [_infer_shape(inputs[0], mod)[i] for i in (0, 4, 1, 2, 3)]
            inputs[0] = _op.transpose(inputs[0], axes=(0, 4, 1, 2, 3))
            attr['data_format'] = "NCDHW"
            flip_layout = True

        attr['padding'] = attr['padding'].decode("utf-8")

        if attr['padding'] == 'VALID':
            attr['padding'] = [0, 0, 0, 0, 0, 0]
        elif attr['padding'] == 'SAME':
            stride_d, stride_h, stride_w = attr['strides']
            kernel_d, kernel_h, kernel_w = attr['kernel_shape']
            if attr['data_format'] == 'NDHWC':
                in_d = input_shape[1]
                in_h = input_shape[2]
                in_w = input_shape[3]
            else:
                in_d = input_shape[2]
                in_h = input_shape[3]
                in_w = input_shape[4]
            pad_d = _get_pad_pair(in_d, kernel_d, stride_d)
            pad_v = _get_pad_pair(in_h, kernel_h, stride_h)
            pad_h = _get_pad_pair(in_w, kernel_w, stride_w)

            attr['padding'] = [pad_d[0], pad_v[0], pad_h[0], pad_d[1], pad_v[1], pad_h[1]]
        else:
            msg = 'Value {} in attribute "padding" of operator Pooling is ' \
                  'not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))

        if name == "avg_pool":
            attr['count_include_pad'] = False
        attr['ceil_mode'] = False
        out = AttrCvt(
            op_name=name,
            transforms={
                'kernel_shape': 'pool_size',
                'data_format': 'layout'},
            ignores=['ksize'])(inputs, attr)
        if flip_layout:
            out = _op.transpose(out, axes=(0, 2, 3, 4, 1))
        return out

    return _impl

def _pooling(name):
    def _impl(inputs, attr, params, mod):

        attr['data_format'] = attr['data_format'].decode("utf-8")
        flip_layout = False

        input_shape = _infer_shape(inputs[0], mod)

        if attr['data_format'] == 'NHWC':
            attr['kernel_shape'] = (attr['ksize'][1], attr['ksize'][2])
            attr['strides'] = (attr['strides'][1], attr['strides'][2])
        elif attr['data_format'] == 'NCHW':
            attr['kernel_shape'] = (attr['ksize'][2], attr['ksize'][3])
            attr['strides'] = (attr['strides'][2], attr['strides'][3])
        else:
            msg = 'Value {} of attribute "data_format" of operator Pooling ' \
                  'is not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))

        if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
            tmp_shape = _infer_shape(inputs[0], mod)
            input_shape = [tmp_shape[ii] for ii in (0, 3, 1, 2)]
            inputs[0] = _op.transpose(inputs[0], axes=(0, 3, 1, 2))
            attr['data_format'] = "NCHW"
            flip_layout = True

        # Fix padding
        attr['padding'] = attr['padding'].decode("utf-8")

        if attr['padding'] == 'VALID':
            attr['padding'] = [0, 0]
        elif attr['padding'] == 'SAME':
            stride_h, stride_w = attr['strides']
            kernel_h, kernel_w = attr['kernel_shape']
            if attr['data_format'] == 'NHWC':
                in_h = input_shape[1]
                in_w = input_shape[2]
            else:
                in_h = input_shape[2]
                in_w = input_shape[3]

            pad_v = _get_pad_pair(in_h, kernel_h, stride_h)
            pad_h = _get_pad_pair(in_w, kernel_w, stride_w)

            attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
        else:
            msg = 'Value {} in attribute "padding" of operator Pooling is ' \
                  'not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))

        if name == "avg_pool":
            attr['count_include_pad'] = False

        out = AttrCvt(
            op_name=_dimension_picker(name),
            transforms={
                'kernel_shape':'pool_size',
                'data_format':'layout'},
            ignores=['ksize'],
            extras={'ceil_mode': False},
            custom_check=_dimension_constraint())(inputs, attr)

        if flip_layout:
            out = _op.transpose(out, axes=(0, 2, 3, 1))

        return out
    return _impl

def _conv(opname):
    def _impl(inputs, attr, params, mod):
        attr['data_format'] = attr['data_format'].decode("utf-8")
        flip_layout = False

        if opname == 'conv_transpose' and attr['data_format'] == 'NHWC':
            # transform to NCHW for TVM backend compatible and set 'flip_layout'
            # to have output flip back to NHWC
            tmp_shape = _infer_shape(inputs[2], mod)
            tmp_shape = [tmp_shape[ii] for ii in (0, 3, 1, 2)]
            inputs[2] = _op.transpose(inputs[2], axes=(0, 3, 1, 2))
            attr['strides'][1], attr['strides'][2], attr['strides'][3] = \
                attr['strides'][3], attr['strides'][1], attr['strides'][2]
            attr['data_format'] = 'NCHW'

            if opname == 'conv_transpose' and len(attr['_output_shapes']) > 0:
                tmp_shape = attr['_output_shapes'][0]
                tmp_shape = [tmp_shape[ii] for ii in (0, 3, 1, 2)]
                attr['_output_shapes'][0] = tmp_shape

            flip_layout = True

        inputs_data = inputs[0] if opname != 'conv_transpose' else inputs[2]

        # NCHW Layout require weights transpose
        weights_shape = _infer_shape(inputs[1])
        if attr['data_format'] == 'NCHW':
            tmp_shape = weights_shape
            if opname in ['conv', 'conv_transpose']:
                tmp_shape = [tmp_shape[ii] for ii in (3, 2, 0, 1)]
                inputs[1] = _op.transpose(inputs[1], axes=(3, 2, 0, 1))
            else:
                tmp_shape = [tmp_shape[ii] for ii in (2, 3, 0, 1)]
                inputs[1] = _op.transpose(inputs[1], axes=(2, 3, 0, 1))
            weights_shape = tmp_shape


        input_shape = _infer_shape(inputs_data)
        if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
            input_shape = [input_shape[ii] for ii in (0, 3, 1, 2)]
            inputs_data = _op.transpose(inputs_data, axes=(0, 3, 1, 2))
            if opname in ['conv', 'conv_transpose']:
                weights_shape = [weights_shape[ii] for ii in (3, 2, 0, 1)]
                inputs[1] = _op.transpose(inputs[1], axes=(3, 2, 0, 1))
            else:
                weights_shape = [weights_shape[ii] for ii in (2, 3, 0, 1)]
                inputs[1] = _op.transpose(inputs[1], axes=(2, 3, 0, 1))

            attr['data_format'] = "NCHW"
            attr['strides'] = [attr['strides'][ii] for ii in (0, 3, 1, 2)]
            flip_layout = True

        if attr['data_format'] == 'NHWC':
            in_channels = input_shape[3]
            kernel_h, kernel_w, _, depth_mult = weights_shape
            attr['kernel_shape'] = (weights_shape[0], weights_shape[1])
            if opname == 'conv':
                attr['channels'] = weights_shape[3]
            elif opname == 'conv_transpose':
                attr['channels'] = weights_shape[2]
            else:
                attr['channels'] = input_shape[3] * depth_mult

            if 'dilations' in attr:
                attr['dilations'] = (attr['dilations'][1], attr['dilations'][2])
            attr['strides'] = (attr['strides'][1], attr['strides'][2])
        elif attr['data_format'] == 'NCHW':
            in_channels = input_shape[1]
            _, depth_mult, kernel_h, kernel_w = weights_shape
            attr['kernel_shape'] = (weights_shape[2], weights_shape[3])
            if opname == 'conv':
                attr['channels'] = weights_shape[0]
            elif opname == 'conv_transpose':
                attr['channels'] = weights_shape[1]
            else:
                attr['channels'] = input_shape[1] * depth_mult
                if attr['channels'] < 0:
                    attr['channels'] *= -1

            if 'dilations' in attr:
                attr['dilations'] = (attr['dilations'][2], attr['dilations'][3])
            attr['strides'] = (attr['strides'][2], attr['strides'][3])
        else:
            msg = 'Value {} in attribute "data_format" of operator Conv is ' \
                  'not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))

        if opname == 'depthwise':
            attr['groups'] = in_channels

        # Fix padding
        attr['padding'] = attr['padding'].decode("utf-8")

        if attr['padding'] == 'VALID':
            attr['padding'] = [0, 0]
        elif attr['padding'] == 'SAME':
            stride_h, stride_w = attr['strides']
            kernel_h, kernel_w = attr['kernel_shape']

            pdata_shape = input_shape
            if opname == 'conv_transpose' and len(attr['_output_shapes']) > 0:
                pdata_shape = attr['_output_shapes'][0]

            if attr['data_format'] == 'NHWC':
                in_h = pdata_shape[1]
                in_w = pdata_shape[2]
            else:
                in_h = pdata_shape[2]
                in_w = pdata_shape[3]

            dilation_h = attr['dilations'][0]
            dilation_w = attr['dilations'][1]
            dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
            dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
            pad_v = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
            pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)

            attr['padding'] = [pad_v[0], pad_h[0], pad_v[1], pad_h[1]]
        else:
            msg = 'Value {} in attribute "padding" of operator Conv is not ' \
                  'valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))

        if 'kernel_layout' not in attr:
            if opname in ['conv', 'conv_transpose']:
                attr['kernel_layout'] = 'HWIO' if attr['data_format'] == 'NHWC' else 'OIHW'
            else:
                attr['kernel_layout'] = 'HWOI' if attr['data_format'] == 'NHWC' else 'OIHW'

        use_bias = len(inputs) == (3 if opname != 'conv_transpose' else 4)
        channel_axis = 1 if attr['data_format'] == "NCHW" else 3

        # Ignore the new attributes from TF2.0, for now.
        out = AttrCvt(
            op_name=_dimension_picker('conv',
                                      surfix="_transpose" if opname == 'conv_transpose' else ""),
            ignores=['explicit_paddings'],
            transforms={
                'kernel_shape': 'kernel_size',
                'data_format': 'data_layout',
                'dilations': ('dilation', (0, 0)),
                'group': ('groups', 1)},
            custom_check=_dimension_constraint())([inputs_data, inputs[1]], attr)

        if use_bias:
            out = _op.nn.bias_add(out,
                                  inputs[2] if opname != 'conv_transpose' else inputs[3],
                                  axis=channel_axis)

        if flip_layout:
            out = _op.transpose(out, axes=(0, 2, 3, 1))

        return out
    return _impl


# Dilation2d
def _dilation2d():
    def _impl(inputs, attr, params, mod):
        if 'data_format' not in attr:
            attr['data_format'] = 'NHWC'

        input_shape = _infer_shape(inputs[0], mod)
        weights_shape = _infer_shape(inputs[1], mod)

        if attr['_target_layout'] == "NCHW" and attr['data_format'] == "NHWC":
            input_shape = [input_shape[ii] for ii in (0, 3, 1, 2)]
            inputs[0] = _op.transpose(inputs[0], axes=(0, 3, 1, 2))
            weights_shape = [weights_shape[ii] for ii in (2, 0, 1)]
            inputs[1] = _op.transpose(inputs[1], axes=(2, 0, 1))
            attr['data_format'] = "NCHW"

        if attr['data_format'] in ['NHWC', 'NCHW']:
            if 'rates' in attr:
                attr['dilations'] = attr['rates']
            if 'dilations' in attr:
                attr['dilations'] = (attr['dilations'][1], attr['dilations'][2])
            attr['strides'] = (attr['strides'][1], attr['strides'][2])
        else:
            msg = 'Value {} in attribute "data_format" of operator Dilation2D is ' \
                  'not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))

        attr['padding'] = attr['padding'].decode("utf-8")
        if attr['padding'] == 'VALID':
            attr['padding'] = [0, 0]
        elif attr['padding'] == 'SAME':
            stride_h, stride_w = attr['strides']
            if attr['data_format'] == 'NHWC':
                kernel_h, kernel_w = weights_shape[0], weights_shape[1]
            else:
                kernel_h, kernel_w = weights_shape[1], weights_shape[2]
            if attr['data_format'] == 'NHWC':
                in_h = input_shape[1]
                in_w = input_shape[2]
            else:
                in_h = input_shape[2]
                in_w = input_shape[3]

            dilation_h = attr['dilations'][0]
            dilation_w = attr['dilations'][1]
            dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
            dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
            pad_v = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
            pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)

            if attr['data_format'] == 'NHWC':
                inputs[0] = _op.nn.pad(data=inputs[0],
                                       pad_width=((0, 0),
                                                  (pad_v[0], pad_v[1]),
                                                  (pad_h[0], pad_h[1]),
                                                  (0, 0)))
            else:
                inputs[0] = _op.nn.pad(data=inputs[0],
                                       pad_width=((0, 0),
                                                  (0, 0),
                                                  (pad_v[0], pad_v[1]),
                                                  (pad_h[0], pad_h[1])))

            attr['padding'] = [0, 0]

        else:
            msg = 'Value {} in attribute "padding" of operator Dilation2d is not ' \
                  'valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))

        attr['kernel_layout'] = 'HWI' if attr['data_format'] == 'NHWC' else 'IHW'
        out = AttrCvt(
            op_name='dilation2d',
            ignores=['explicit_paddings', 'rates'],
            transforms={
                'data_format': 'data_layout',
            })([inputs[0], inputs[1]], attr)
        if attr['_target_layout'] == "NCHW":
            out = _op.transpose(out, axes=(0, 2, 3, 1))
        return out

    return _impl


def _conv3d(opname):
    def _impl(inputs, attr, params, mod):
        attr['data_format'] = attr['data_format'].decode("utf-8")
        flip_layout = False

        inputs_data = inputs[0] if opname != 'conv_transpose' else inputs[2]

        # NCDHW Layout require weights transpose
        weights_shape = _infer_shape(inputs[1], mod)
        if attr['data_format'] == 'NCDHW':
            tmp_shape = weights_shape
            tmp_shape = [tmp_shape[ii] for ii in (4, 3, 0, 1, 2)]
            inputs[1] = _op.transpose(inputs[1], axes=(4, 3, 0, 1, 2))
            weights_shape = tmp_shape

        input_shape = _infer_shape(inputs_data, mod)

        if attr['_target_layout'] == "NCDHW" and attr['data_format'] == "NDHWC":
            input_shape = [input_shape[ii] for ii in (0, 4, 1, 2, 3)]
            inputs_data = _op.transpose(inputs_data, axes=(0, 4, 1, 2, 3))
            weights_shape = [weights_shape[ii] for ii in (4, 3, 0, 1, 2)]
            inputs[1] = _op.transpose(inputs[1], axes=(4, 3, 0, 1, 2))

            attr['data_format'] = "NCDHW"
            attr['strides'] = [attr['strides'][ii] for ii in (0, 4, 1, 2, 3)]
            flip_layout = True

        if attr['data_format'] == 'NDHWC':
            kernel_d, kernel_h, kernel_w, _, _ = weights_shape
            attr['kernel_shape'] = (kernel_d, kernel_h, kernel_w)
            if opname == 'conv':
                attr['channels'] = weights_shape[4]
            elif opname == 'conv_transpose':
                attr['channels'] = weights_shape[3]

            if 'dilations' in attr:
                attr['dilations'] = \
                    (attr['dilations'][1], attr['dilations'][2], attr['dilations'][3])
            attr['strides'] = (attr['strides'][1], attr['strides'][2], attr['strides'][3])
        elif attr['data_format'] == 'NCDHW':
            _, _, kernel_d, kernel_h, kernel_w = weights_shape
            attr['kernel_shape'] = (kernel_d, kernel_h, kernel_w)
            if opname == 'conv':
                attr['channels'] = weights_shape[0]
            elif opname == 'conv_transpose':
                attr['channels'] = weights_shape[1]

            if 'dilations' in attr:
                attr['dilations'] = \
                    (attr['dilations'][2], attr['dilations'][3], attr['dilations'][4])
            attr['strides'] = (attr['strides'][2], attr['strides'][3], attr['strides'][4])
        else:
            msg = 'Value {} in attribute "data_format" of operator Conv is ' \
                  'not valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['data_format']))

        # Fix padding
        attr['padding'] = attr['padding'].decode("utf-8")

        if attr['padding'] == 'VALID':
            attr['padding'] = [0, 0, 0]
        elif attr['padding'] == 'SAME':
            stride_d, stride_h, stride_w = attr['strides']
            kernel_d, kernel_h, kernel_w = attr['kernel_shape']

            pdata_shape = input_shape
            if opname == 'conv_transpose' and len(attr['_output_shapes']) > 0:
                pdata_shape = attr['_output_shapes'][0]

            if attr['data_format'] == 'NDHWC':
                in_d = pdata_shape[1]
                in_h = pdata_shape[2]
                in_w = pdata_shape[3]
            else:
                in_d = pdata_shape[2]
                in_h = pdata_shape[3]
                in_w = pdata_shape[4]

            dilation_d = attr['dilations'][0]
            dilation_h = attr['dilations'][1]
            dilation_w = attr['dilations'][2]
            dilated_kernel_d = (kernel_d - 1) * dilation_d + 1
            dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
            dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
            pad_d = _get_pad_pair(in_d, dilated_kernel_d, stride_d)
            pad_v = _get_pad_pair(in_h, dilated_kernel_h, stride_h)
            pad_h = _get_pad_pair(in_w, dilated_kernel_w, stride_w)

            attr['padding'] = [pad_d[0], pad_v[0], pad_h[0], pad_d[1], pad_v[1], pad_h[1]]

        else:
            msg = 'Value {} in attribute "padding" of operator Conv is not ' \
                  'valid.'
            raise tvm.error.OpAttributeInvalid(msg.format(attr['padding']))

        if 'kernel_layout' not in attr:
            attr['kernel_layout'] = 'DHWIO' if attr['data_format'] == 'NDHWC' else 'OIDHW'

        use_bias = len(inputs) == (3 if opname != 'conv_transpose' else 4)
        channel_axis = 1 if attr['data_format'] == "NCDHW" else 4

        # Ignore the new attributes from TF2.0, for now.
        out = AttrCvt(
            op_name=_dimension_picker('conv',
                                      surfix="_transpose" if opname == 'conv_transpose' else ""),
            ignores=['explicit_paddings'],
            transforms={
                'kernel_shape': 'kernel_size',
                'data_format': 'data_layout',
                'dilations': ('dilation', (0, 0)),
                'group': ('groups', 1)},
            custom_check=_dimension_constraint())([inputs_data, inputs[1]], attr)

        if use_bias:
            out = _op.nn.bias_add(out,
                                  inputs[2] if opname != 'conv_transpose' else inputs[3],
                                  axis=channel_axis)

        if flip_layout:
            out = _op.transpose(out, axes=(0, 2, 3, 4, 1))

        return out
    return _impl

def _decode_image():
    def _impl(inputs, attr, params, mod):
        # Image decode wrapper: Expecting user to feed decoded input to next layer drop this layer.
        warnings.warn("DecodeJpeg: It's a pass through, please handle preprocessing before input")
        return inputs[0]
    return _impl

def _unravel_index():
    def _impl(inputs, attr, params, mod):
        return _op.unravel_index(inputs[0], inputs[1])
    return _impl

def _crop_and_resize():
    def _impl(inputs, attr, params, mod):
        # input image is a 4-D tensor of shape [batch, image_height, image_width, depth]
        # boxes is a 2-D tensor of shape [num_boxes, 4], 4 is for [y1, x1, y2, x2]
        try:
            crop_size = _get_list_param(params, inputs[3])
        except (IndexError, KeyError):
            crop_size = _infer_value(inputs[3], params).asnumpy().tolist()

        method = attr['method'].decode()
        method = 'nearest_neighbor' if method == 'nearest' else method
        if method not in ['bilinear', 'nearest_neighbor']:
            raise tvm.error.OpAttributeUnImplemented(
                'Method {} is not supported'.format(method))
        layout = attr['layout'] if 'layout' in attr else 'NHWC'
        extrapolation_value = attr['extrapolation_value']

        return get_relay_op("crop_and_resize")(inputs[0], inputs[1], inputs[2], crop_size,
                                               layout, method, extrapolation_value)
    return _impl

def _cast():
    def _impl(inputs, attr, params, mod):
        return inputs[0].astype(attr['DstT'].name)
    return _impl

def _expand_dims():
    def _impl(inputs, attr, params, mod):
        dim_input = inputs.pop(1)
        axis = _get_num_param(params, dim_input)
        return AttrCvt(op_name="expand_dims", ignores=['Tdim', 'N'],
                       extras={'axis': int(axis), 'num_newaxis': 1})(inputs, attr)
    return _impl

def _resize(method):
    def _impl(inputs, attr, params, mod):
        if attr['_output_shapes'][0] is not None:
            size = attr['_output_shapes'][0][1:3]
            # Important that the size is defined. If an axis is not, we need to infer what
            # the shape should be.
            if -1 in size:
                size = _infer_value(inputs[1], params).asnumpy().reshape([-1]).tolist()
        else:
            size = _infer_value(inputs[1], params).asnumpy().reshape([-1]).tolist()

        attr['size'] = size
        inputs.pop(1)
        # NHWC
        attr['layout'] = 'NHWC'
        if attr.pop('align_corners') is True:
            attr['coordinate_transformation_mode'] = 'align_corners'
        else:
            attr['coordinate_transformation_mode'] = 'asymmetric'

        # Ignore the new attributes from TF2.0, for now.
        return AttrCvt(op_name='resize',
                       ignores=['Tdim', 'half_pixel_centers'],
                       extras={'method': method})(inputs, attr)
    return _impl

def _check_numerics():
    def _impl(inputs, attr, params, mod):
        # Making a copy node assuming no need to verify
        return AttrCvt(op_name="copy", ignores=['message'])(inputs, attr)
    return _impl

def _assert():
    # ToDo: In general people want asserts to be gone from TensorFlow graphs
    # when they are optimizing them, so converting it to a no-op is
    # reasonable. However, it would be nice to have the option to keep them
    # once Relay gets a Halt or Assert op.
    return _no_op()

def _no_op():
    def _impl(inputs, attr, params, mod):
        # ToDo: This should really be an op that returns nothing, which could
        # be represented as an empty tuple. It turns out that TVM
        # infrastructure doesn't like running functions that return None and
        # also don't like running functions that return an empty tuple. So it
        # doesn't work, but it should be made to work and then this could be
        # improved. In the mean time, it is hard to imagine a case where it
        # matters in any real way that a no-op is converted to a constant 0.
        return tvm.relay.const(0)
    return _impl

def _matmul():
    def _impl(inputs, attr, params, mod):
        channels = _infer_channels(inputs[1], not attr['transpose_b'])
        if attr['transpose_a']:
            inputs[0] = _op.transpose(inputs[0], axes=(1, 0))
        if not attr['transpose_b']:
            inputs[1] = _op.transpose(inputs[1], axes=(1, 0))
        return AttrCvt(op_name="dense",
                       extras={'units': channels},
                       ignores=['transpose_a', 'transpose_b', 'T'])(inputs, attr)

    return _impl

def _batch_matmul():
    def _impl(inputs, attr, params, mod):
        input_x = inputs[0]
        input_y = inputs[1]
        orig_shape_x = _infer_shape(input_x, mod)
        orig_shape_y = _infer_shape(input_y, mod)

        # reshape n-dimensional batch matmul into 3d
        if len(orig_shape_x) > 3:
            outer_dims = [orig_shape_x[i] for i in range(0, len(orig_shape_x) - 2)]
            num_outer_elts = np.prod(outer_dims)
            new_shape_x = (num_outer_elts, orig_shape_x[-2], orig_shape_x[-1])
            new_shape_y = (num_outer_elts, orig_shape_y[-2], orig_shape_y[-1])
            input_x = _op.reshape(input_x, newshape=new_shape_x)
            input_y = _op.reshape(input_y, newshape=new_shape_y)

        adj_x = attr['adj_x']
        adj_y = attr['adj_y']
        input_x = _op.transpose(input_x, axes=[0, 2, 1]) if adj_x else input_x
        input_y = _op.transpose(input_y, axes=[0, 2, 1]) if not adj_y else input_y
        ret = get_relay_op('batch_matmul')(input_x, input_y)

        # reshape result back to n-dimensional
        if len(orig_shape_x) > 3:
            final_shape = list(orig_shape_x)
            final_shape[-2] = orig_shape_x[-1] if adj_x else orig_shape_x[-2]
            final_shape[-1] = orig_shape_y[-2] if adj_y else orig_shape_y[-1]
            ret = _op.reshape(ret, newshape=final_shape)

        return ret
    return _impl

def _identity():
    def _impl(inputs, attr, params, mod):
        return inputs[0]
    return _impl

def _concatV2():
    def _impl(inputs, attr, params, mod):
        pop_node = inputs.pop(len(inputs)-1)
        axis = int(_get_num_param(params, pop_node))
        return AttrCvt(
            op_name="concatenate", ignores=['T', 'N', 'Tidx'],
            extras={'axis': axis})([inputs], attr)
    return _impl

def _concat():
    def _impl(inputs, attr, params, mod):
        pop_node = inputs.pop(0)
        axis = int(_get_num_param(params, pop_node))
        return AttrCvt(
            op_name="concatenate", ignores=['N'],
            extras={'axis': axis})([inputs], attr)
    return _impl

def _pack():
    def _impl(inputs, attr, params, mod):
        axis = int(attr["axis"])
        inputs_reshaped = [_op.expand_dims(i, axis=axis, num_newaxis=1) for i in inputs]
        return _op.concatenate(inputs_reshaped, axis)
    return _impl

def _tensor_array():
    def _impl(inputs, attr, params, prelude):
        dtype_str = attr.get('dtype').name
        tensor_array_constructor = prelude.get_var('tensor_array', dtype_str)
        return tensor_array_constructor(_op.take(inputs[0], tvm.relay.const(0)))
    return _impl

def _tensor_array_scatter():
    def _impl(inputs, attr, params, prelude):
        dtype_str = attr.get('T').name
        values_rank = len(inputs[2].type_annotation.shape)
        unstack_name = "tensor_array_unstack_tensor{}".format(values_rank)
        unstack_function = prelude.get_var(unstack_name, dtype_str)
        values = unstack_function(inputs[2])
        tensor_array_scatter_func = prelude.get_var('tensor_array_scatter', dtype_str)
        return tensor_array_scatter_func(inputs[0], inputs[1], values)
    return _impl

def _tensor_array_gather():
    def _impl(inputs, attr, params, prelude):
        return prelude.tensor_array_gather(inputs[2], inputs[1])
    return _impl

def _tensor_array_size():
    def _impl(inputs, attr, params, prelude):
        return prelude.length(inputs[0])
    return _impl

def _tensor_array_write():
    def _impl(inputs, attr, params, prelude):
        input_rank = len(inputs[2].type_annotation.shape)
        dtype = attr.get('T').name

        tensor_name = 'tensor{}'.format(input_rank)
        tensor_func = prelude.get_var(tensor_name, dtype)
        v = tensor_func(inputs[2])
        write_func = prelude.get_var('tensor_array_write', dtype)

        return write_func(inputs[3], _op.take(inputs[1], tvm.relay.const(0)), v)
    return _impl

def _tensor_array_read():
    def _impl(inputs, attr, params, prelude):
        read_func = prelude.get_var('tensor_array_read', attr.get('dtype').name)
        return read_func(inputs[2], _op.take(inputs[1], tvm.relay.const(0)))
    return _impl

def _tensor_array_split():
    def _impl(inputs, attr, params, prelude):
        input_rank = len(inputs[1].type_annotation.shape)
        dtype_str = attr.get('T').name
        v = prelude.get_var("tensor{}".format(input_rank), dtype_str)(inputs[1])
        lengths = _op.cast(inputs[2], 'int32')
        split_var = prelude.get_var('tensor_array_split', dtype_str)
        return split_var(inputs[0], v, lengths)
    return _impl

def _tensor_array_concat():
    def _impl(inputs, attr, params, prelude):
        concat_func = prelude.get_var('tensor_array_concat', attr['dtype'].name)
        return concat_func(inputs[1])
    return _impl

def _tile():
    def _impl(inputs, attr, params, mod):
        reps = _get_list_param(params, inputs.pop())
        new_input = []
        new_input.append(inputs.pop(0))

        return AttrCvt(
            op_name='tile',
            extras={'reps': tuple(reps)},
            ignores=['Tmultiples'])(new_input, attr)
    return _impl

def _slice():
    def _impl(inputs, attr, params, mod):
        try:
            begin = _get_list_param(params, inputs[1])
        except (IndexError, KeyError, AttributeError):
            begin = _infer_value(inputs[1], params).asnumpy().tolist()[0]
        try:
            size = _get_list_param(params, inputs[2])
        except (IndexError, KeyError, AttributeError):
            # Handle symbolic size
            try:
                size = _infer_value(inputs[2], params).asnumpy().tolist()[0]
            except Exception:
                size = inputs[2]
        data_shape = _infer_shape(inputs[0], mod)
        data_dim = len(data_shape)
        end = size
        if not isinstance(end, (_expr.Call, _expr.Var)):
            for i in range(data_dim):
                if size[i] == -1:
                    end[i] = data_shape[i]
                else:
                    end[i] += begin[i]
        return _op.strided_slice(inputs[0], begin=begin, end=end)
    return _impl


def _reshape():
    def _impl(inputs, attr, params, mod):
        pop_node = inputs.pop(1)

        try:
            shape_arg = _get_tuple_param(params, pop_node)
        except AttributeError:
            # Shape operator is already pruned, hence
            # try to infer shape by precompute prune if possible.
            try:
                params_new = _infer_value(pop_node, params)
                shape_arg = tuple(params_new.asnumpy().astype('int64').flatten())
            except Exception:
                # Deal with symbolic shape case.
                # Currently only shape_of can be the direct ancestor.
                if not isinstance(pop_node, tvm.relay.expr.Call) or \
                        "shape_of" not in str(pop_node.op):
                    raise RuntimeError("If shape operator is used in reshape to "
                                       "express reshape_like, shape_of must be "
                                       "the direct ancestor of reshape when input "
                                       "shape is symbolic.")
                return _op.reshape_like(inputs[0], pop_node.args[0])
        return AttrCvt(
            op_name="reshape",
            extras={'newshape': shape_arg},
            ignores=['Tshape'])(inputs, attr)
    return _impl


def _depth_to_space():
    def _impl(inputs, attr, params, mod):
        block_size = int(attr['block_size'])
        layout = attr['data_format'].decode("utf-8")
        return _op.nn.depth_to_space(inputs[0], block_size, layout)

    return _impl


def _space_to_depth():
    def _impl(inputs, attr, params, mod):
        block_size = int(attr['block_size'])
        layout = attr['data_format'].decode("utf-8")
        return _op.nn.space_to_depth(inputs[0], block_size, layout)

    return _impl


def _bias_add():
    def _impl(inputs, attr, params, mod):
        # Must expand for proper broadcasting in NCHW.
        if attr['data_format'].decode("utf-8") == 'NCHW':
            bias = _op.reshape(inputs[1], newshape=(1, -1, 1, 1))
        else:
            bias = inputs[1]
        return _op.add(inputs[0], bias)
    return _impl

def _broadcast_to():
    def _impl(inputs, attr, params, mod):
        if isinstance(inputs[1], _expr.Var):
            shape = params[inputs[1].name_hint]
        else:
            shape = _infer_value(inputs[1], params)
        shape = list(shape.asnumpy().reshape([-1]))
        return _op.broadcast_to(inputs[0], shape)
    return _impl

def _squeeze():
    def _impl(inputs, attr, params, mod):
        if len(attr['squeeze_dims']) == 0:
            attr['squeeze_dims'] = None
        return AttrCvt(
            op_name="squeeze",
            transforms={'squeeze_dims':'axis'},
            ignores=['T'])(inputs, attr)
    return _impl

def _fused_batch_norm():
    def _impl(inputs, attr, params, mod):
        # Tensorflow: (data, gamma, beta, moving_mean, moving_variance)
        # Relay:       (data, gamma, beta, moving_mean, moving_varience)
        assert len(inputs) == 5
        axis = 3
        need_cast = False

        if 'data_format' in attr:
            attr['data_format'] = attr['data_format'].decode("utf-8")
            if attr['data_format'] == 'NCHW':
                axis = 1
        if 'U' in attr:
            need_cast = True
            inputs[0] = _op.cast(inputs[0], dtype=attr['U'].name)
        # Check if mean and variance are empty
        # If so, replace them with Mean and Variance Ops
        # For run-time calculation
        moving_mean_shape = [int(n) for n in inputs[3].type_annotation.shape]
        moving_variance_shape = [int(n) for n in inputs[4].type_annotation.shape]
        if (moving_mean_shape[0] == 0 and moving_variance_shape[0] == 0):
            inputs[3] = _op.mean(inputs[0], axis=axis, keepdims=False, exclude=True)
            inputs[4] = _op.variance(inputs[0], axis=axis, keepdims=False, exclude=True)
        out = AttrCvt(op_name='batch_norm',
                      transforms={'scale_after_normalization':'scale',
                                  'variance_epsilon':'epsilon'},
                      extras={'axis': axis},
                      ignores=['data_format', 'U'],
                      disables=['momentum'])(inputs, attr)

        if need_cast:
            out = _expr.TupleGetItem(out.astuple(), 0)
            out = _op.cast(out, dtype=attr['T'].name)
        return out
    return _impl

def _batch_norm():
    def _impl(inputs, attr, params, mod):
        # Rearrange inputs from
        # (data, moving_mean, moving_variance, beta, gamma)
        #     to
        # (data, gamma, beta, moving_mean, moving_var)
        new_inputs = [inputs[0], inputs[4], inputs[3], inputs[1], inputs[2]]

        axis = 3
        if 'data_format' in attr:
            attr['data_format'] = attr['data_format'].decode("utf-8")
            if attr['data_format'] == 'NCHW':
                axis = 1

        return AttrCvt(
            op_name='batch_norm',
            transforms={'scale_after_normalization':'scale', 'variance_epsilon':'epsilon'},
            extras={'axis': axis},
            ignores=['data_format'],
            disables=['momentum'])(new_inputs, attr)
    return _impl

def _relu6():
    def _impl(inputs, attr, params, mod):
        return _op.clip(inputs[0], a_min=0, a_max=6)
    return _impl

def _shape():
    def _impl(inputs, attr, params, mod):
        is_symbolic_shape = False
        input_shape = _infer_shape(inputs[0], mod)
        for axis in input_shape:
            if not isinstance(axis, (int, tvm.tir.IntImm)):
                is_symbolic_shape = True
                break

        if is_symbolic_shape:
            ret = _op.shape_of(inputs[0], dtype='int32')
        else:
            ret = np.array(input_shape, dtype='int32')
        return ret

    return _impl

def _fill():
    def _impl(inputs, attr, params, mod):
        output_shape = attr['_output_shapes'][0]
        # Output shape must be defined to avoid errors. If any axis is not, we must
        # try to compute its shape.
        if output_shape is None or -1 in output_shape:
            output_shape = _infer_value(inputs[0], params).asnumpy().reshape([-1]).tolist()

        fill_arg = _get_num_param(params, inputs.pop(1))
        dtype = attr['T'].name
        return _op.full(tvm.relay.const(fill_arg, dtype),
                        output_shape, dtype)
    return _impl

def _lrn():
    def _impl(inputs, attr, params, mod):
        attr_new = {}
        depth_radius = attr.get('depth_radius', 5)
        size = (depth_radius * 2) + 1
        attr_new['axis'] = 3 # Fix axis, NHWC format
        attr_new['size'] = size
        attr_new['bias'] = attr.get('bias', 1)
        attr_new['alpha'] = attr.get('alpha', 1) * size
        attr_new['beta'] = attr.get('beta', 0.5)
        return AttrCvt(op_name='lrn')(inputs, attr_new)
    return _impl

def _sum():
    def _impl(inputs, attr, params, mod):
        axis = _get_tuple_param(params, inputs[1])
        return AttrCvt(
            op_name='sum',
            extras={'axis': axis},
            transforms={'keep_dims':'keepdims'},
            ignores=['name', 'Tidx'])([inputs[0]], attr)
    return _impl

def _reduce(op):
    def _impl(inputs, attr, params, mod):
        axis = _get_list_param(params, inputs[1])
        axis = tuple(axis)
        return AttrCvt(
            op_name=op,
            extras={'axis': axis},
            transforms={'keep_dims':'keepdims'},
            ignores=['name', 'Tidx'])([inputs[0]], attr)
    return _impl

def _square():
    def _impl(inputs, attr, params, mod):
        return _op.multiply(inputs[0], inputs[0])
    return _impl

def _gather():
    "GatherV2, Gather"
    def _impl(inputs, attr, params, mod):
        if len(inputs) > 2:
            axis = _get_num_param(params, inputs.pop(2))
        else:
            axis = 0
        if int(attr.get('batch_dims', 0)) != 0:
            raise tvm.error.OpAttributeUnImplemented(
                'Attribute batch_dims is not supported')
        new_input = inputs[0:2]
        return AttrCvt(op_name="take",
                       extras={'axis': tvm.tir.const(axis, 'int32')},
                       ignores=['Tindices', 'Tparams', 'validate_indices',
                                'Taxis', '_class', 'batch_dims'])(new_input, attr)
    return _impl

def _gather_nd():
    """GatherNd"""
    def _impl(inputs, attr, params, mod):
        return AttrCvt(op_name="gather_nd",
                       ignores=['Tindices', 'Tparams',\
                                'Taxis', '_class'])(inputs, attr)
    return _impl

def _stridedSlice():
    def _impl(inputs, attr, params, mod):
        """Strided Slice.
        Operator description: https://www.tensorflow.org/api_docs/python/tf/strided_slice
        Tensorflow mask validation: https://github.com/tensorflow/tensorflow/blob/master/
        tensorflow/core/util/strided_slice_op.cc#L147-L368
        """
        begin = _get_list_param(params, inputs[1])
        end = _get_list_param(params, inputs[2])
        stride = _get_list_param(params, inputs[3])
        begin_mask = int(attr.get('begin_mask', 0))
        end_mask = int(attr.get('end_mask', 0))
        ellipsis_mask = int(attr.get('ellipsis_mask', 0))
        new_axis_mask = int(attr.get('new_axis_mask', 0))
        shrink_axis_mask = int(attr.get('shrink_axis_mask', 0))
        data_shape = _infer_shape(inputs[0], mod)
        data_dim = len(data_shape)
        stride_dim = len(stride)

        def _transform_mask(stride_dim, ellipsis_mask):
            """Handle mask inputs to create new begin, end, stride and output shape"""
            m_begin = [0] * data_dim
            m_end = [0] * data_dim
            m_stride = [0] * data_dim
            fshape_indices = []
            #Count new axis after ellipsis_mask, consider while applying ellipsis_mask.
            ellipsis_seen = False
            new_axes_after_ellipsis = 0
            for i in range(stride_dim):
                mask = 1 << i
                if ellipsis_seen and (mask & new_axis_mask) != 0:
                    new_axes_after_ellipsis += 1
                if (mask & ellipsis_mask) != 0:
                    ellipsis_seen = True
            if not ellipsis_seen:
                #Used later for extending the stride attributes in the below loop.
                ellipsis_mask |= (1 << stride_dim)
                stride_dim += 1
            final_index = 0
            for index in range(stride_dim):
                mask = 1 << index
                if mask & ellipsis_mask:
                    #Identify the end index for applying ellipsis_mask
                    to_index = min(((data_dim - (stride_dim-index)) + 1
                                    + new_axes_after_ellipsis), data_dim)
                    for i in range(final_index, to_index):
                        m_begin[final_index] = 0
                        m_end[final_index] = data_shape[final_index]
                        m_stride[final_index] = 1
                        fshape_indices.append(final_index)
                        final_index += 1
                elif mask &new_axis_mask:
                    fshape_indices.append(-1)
                elif not mask & new_axis_mask:
                    if final_index == len(m_begin):
                        break
                    if mask & begin_mask:
                        m_begin[final_index] = data_shape[final_index] \
                                                     if stride[index] < 0 else 0
                    elif begin[index]:
                        m_begin[final_index] = begin[index]
                    if mask & end_mask:
                        m_end[final_index] = 0 if stride[index] < 0 \
                                                 else data_shape[final_index]
                    elif end[index]:
                        m_end[final_index] = end[index]
                    m_stride[final_index] = stride[index]
                    if mask & shrink_axis_mask:
                        #Tensorflow make axis with shrink_axis_mask as dimension 1
                        m_begin[final_index] = data_shape[final_index] + begin[index] \
                                                 if begin[index] < 0 else begin[index]
                        m_end[final_index] = begin[index] + 1
                        m_stride[final_index] = 1
                        fshape_indices.append(-2)
                    else:
                        fshape_indices.append(final_index)

                    final_index += 1
            return m_begin, m_end, m_stride, fshape_indices

        fshape_indices = None
        if begin_mask or end_mask or ellipsis_mask or new_axis_mask or shrink_axis_mask:
            begin, end, stride, fshape_indices = _transform_mask(stride_dim, ellipsis_mask)
        out = _op.strided_slice(inputs[0], begin=begin, end=end, strides=stride)
        out_shape = _infer_shape(out, mod)
        if not fshape_indices:
            fshape_indices = range(len(out_shape))

        #Create final output shape.
        final_output = []
        for gather_index in fshape_indices:
            if gather_index == -1:
                final_output.append(1)
            elif gather_index == -2:
                pass
            else:
                final_output.append(out_shape[gather_index])

        if not final_output:
            if not shrink_axis_mask:
                ret = out
            else:
                final_shape = []
                for dim in out_shape:
                    if dim != 1:
                        final_shape.append(dim)
                if len(final_shape) == 0:
                    ret = _op.squeeze(out)
                else:
                    # We need reshape to handle dynamic shape.
                    ret = _op.reshape(out, newshape=tuple(final_shape))
        else:
            ret = _op.reshape(out, newshape=tuple(final_output))
        return ret
    return _impl

def _pad(name):
    def _impl(inputs, attr, params, mod):
        padlist = _get_param(params, inputs[1])
        paddings = tuple(tuple(l) for l in padlist)
        attr['pad_width'] = paddings
        attr['pad_value'] = 0
        new_inputs = [inputs[0]]
        if name == 'PadV2':
            constant_values = _get_num_param(params, inputs[2])
            attr['pad_value'] = constant_values
        return AttrCvt(
            op_name='pad',
            ignores=['Tpaddings'],)(new_inputs, attr)
    return _impl

def _mirror_pad():
    def _impl(inputs, attr, params, mod):
        padlist = _get_param(params, inputs[1])
        paddings = tuple(tuple(l) for l in padlist)
        attr['pad_width'] = paddings
        mode = attr['mode'].decode('utf-8')
        attr['mode'] = mode
        new_inputs = [inputs[0]]
        return AttrCvt(
            op_name='mirror_pad',
            ignores=['Tpaddings'],)(new_inputs, attr)
    return _impl

def _transpose():
    def _impl(inputs, attr, params, mod):
        # If perm is not specified, axes is left empty,
        # otherwise its value is get from params
        try:
            axes = _get_list_param(params, inputs[1])
        except (IndexError, KeyError, AttributeError):
            axes = _infer_value_simulated(inputs[1], params).asnumpy()
        return _op.transpose(inputs[0], axes=axes)
    return _impl

def _where():
    def _impl(inputs, attr, params, mod):
        if len(inputs) == 1:
            return AttrCvt(op_name="argwhere")(inputs, attr)
        return AttrCvt(op_name="where")(inputs, attr)
    return _impl

def _clip_by_value():
    def _impl(inputs, attr, params, mod):
        a_min = _get_num_param(params, inputs[1])
        a_max = _get_num_param(params, inputs[2])
        return _op.clip(inputs[0], a_min=a_min, a_max=a_max)
    return _impl

def _reverse_v2():
    def _impl(inputs, attr, params, mod):
        axis = _get_num_param(params, inputs[1])
        return AttrCvt(
            op_name="reverse",
            ignores=['Tidx'],
            extras={'axis': int(axis)})([inputs[0]], attr)
    return _impl

def _rank():
    def _impl(inputs, attr, params, mod):
        input_shape = _infer_shape(inputs[0], mod)

        name = attr["_node_name"]
        params[name] = tvm.nd.array([len(input_shape)])
        return [_expr.var(name,
                          shape=params[name].shape,
                          dtype='int32')]

    return _impl

def _range():
    def _impl(inputs, attr, params, mod):
        try:
            start = _get_param(params, inputs[0])[0]
        except (IndexError, KeyError, AttributeError):
            try:
                start = _infer_value(inputs[1], params).asnumpy().tolist()
                start = start if not isinstance(start, list) else start[0]
            except Exception:
                # Symbolic start
                start = inputs[0]

        if hasattr(inputs[1], "name_hint") or isinstance(inputs[1], _expr.Constant):
            limit = _get_param(params, inputs[1])[0]
        else:
            if any(['Rank' in param for param in params]):
                limit = params.pop('Rank').asnumpy()[0]
            else:
                try:
                    limit = _infer_value(inputs[1], params, mod).asnumpy().tolist()
                    limit = limit if not isinstance(limit, list) else limit[0]
                except Exception:
                    # Symbolic limit
                    limit = inputs[1]

        try:
            delta = _get_param(params, inputs[2])[0]
        except (IndexError, KeyError, AttributeError):
            try:
                delta = _infer_value(inputs[2], params, mod).asnumpy().tolist()
                delta = delta if not isinstance(delta, list) else delta[0]
            except Exception:
                # Symbolic delta
                delta = inputs[2]


        dtype = attr['Tidx'].name if 'Tidx' in attr else str(start.dtype)
        if isinstance(start, (np.int32, np.int64, int, np.float32, np.float64, float)):
            start = _expr.const(start)
        if isinstance(limit, (np.int32, np.int64, int, np.float32, np.float64, float)):
            limit = _expr.const(limit)
        if isinstance(delta, (np.int32, np.int64, int, np.float32, np.float64, float)):
            delta = _expr.const(delta)

        return AttrCvt(
            op_name="arange",
            ignores=['Tidx'],
            extras={'start': start,
                    'stop': limit,
                    'step': delta,
                    'dtype': dtype})([], attr)
    return _impl

def _elu():
    def _impl(inputs, attr, params, mod):
        dtype = attr['T'].name
        alpha = tvm.relay.const(-1.0, dtype)
        return alpha * _op.nn.relu(tvm.relay.const(1, dtype) \
                                   - _op.exp(inputs[0])) + _op.nn.relu(inputs[0])
    return _impl

def _selu():
    def _impl(inputs, attr, params, mod):
        dtype = attr['T'].name
        alpha = tvm.relay.const(-1.6732632423543772848170429916717, dtype)
        gamma = tvm.relay.const(1.0507009873554804934193349852946, dtype)
        return gamma * (alpha * _op.nn.relu(tvm.relay.const(1, dtype)
                                            - _op.exp(inputs[0])) + _op.nn.relu(inputs[0]))
    return _impl

def _mean():
    def _impl(inputs, attr, params, mod):
        axis = _get_tuple_param(params, inputs[1])
        return AttrCvt(op_name="mean", ignores=['Tdim', 'Tidx'],
                       transforms={'keep_dims': 'keepdims'},
                       extras={'axis': axis})([inputs[0]], attr)
    return _impl

def _broadcast(name):
    def _impl(inputs, attr, params, mod):
        return AttrCvt(
            op_name=name,
            ignores=['name', 'incompatible_shape_error', 'Tidx']
        )(inputs, attr)
    return _impl

def _split(has_size_vector):
    # TF documentation https://www.tensorflow.org/api_docs/python/tf/split
    def _impl(inputs, attr, params, mod):
        try:
            # order and number of inputs are different:
            # if has_size_vector:
            #     https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/split-v
            # else:
            #     https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/split

            # in addition, `axis` and `num_or_size_splits` can be tensors in TensorFlow,
            # we can only support constants
            if has_size_vector:
                input_node_index = 0
                input_axis_index = 2
                size_splits = _get_param(params, inputs[1])
                section_beginnings = np.cumsum(size_splits)[:-1]
                indices_or_sections = tuple(section_beginnings)
            else:
                input_node_index = 1
                input_axis_index = 0
                indices_or_sections = attr['num_split']
            input_node = inputs[input_node_index]
            axis_input_value = _get_num_param(params, inputs[input_axis_index])
        except (IndexError, KeyError):
            raise TypeError(
                "Unsupported argument for split: `axis` and `num_or_size_splits` "
                "should be constants")
        return _op.split(input_node,
                         indices_or_sections=indices_or_sections,
                         axis=int(axis_input_value))
    return _impl

def _unpack():
    def _impl(inputs, attr, params, mod):
        input_node = inputs[0]
        axis = attr['axis']
        input_shape = _infer_shape(input_node, mod)
        axis_length = input_shape[axis]
        if axis_length < 0:
            raise TypeError("Unstack with unknown axis length")
        splitted = _op.split(input_node,
                             indices_or_sections=axis_length,
                             axis=axis)
        axis = [axis]
        return _expr.TupleWrapper(
            _expr.Tuple([_op.squeeze(split_item, axis=axis) \
            for split_item in splitted]), len(splitted))
    return _impl

def _softmax():
    def _impl(inputs, attr, params, mod):
        return AttrCvt(op_name='softmax',
                       transforms={'axis': ('axis', 1)})([inputs[0]], attr)
    return _impl

def _softplus():
    # op description: https://www.tensorflow.org/api_docs/python/tf/math/softplus
    def _impl(inputs, attr, params, mod):
        exp_out = AttrCvt('exp')(inputs, attr)
        inputs.append(tvm.relay.const(1, attr['T'].name))
        rh = tvm.relay.const(1, attr['T'].name)
        add_out = get_relay_op('add')(exp_out, rh)
        return get_relay_op('log')(add_out)
    return _impl

def _topk():
    def _impl(inputs, attr, params, mod):
        k_input = inputs.pop(1)
        try:
            k = int(_get_num_param(params, k_input))
        except (IndexError, KeyError, AttributeError):
            k = int(_infer_value(k_input, params).asnumpy().tolist())
        if k < 1:
            raise tvm.error.OpAttributeInvalid(
                'Attribute k must be positive in operator TopKV2')
        if attr['sorted'] is False:
            raise tvm.error.OpAttributeUnImplemented(
                'Attribute sorted=False is not supported in operator TopKV2')
        return AttrCvt(op_name='topk',
                       ignores=['sorted'],
                       extras={'k': k, 'is_ascend': False, 'dtype': 'int32'})(inputs, attr)
    return _impl

def _floordiv():
    def _impl(inputs, attr, params, mod):
        assert len(inputs) == 2
        return AttrCvt('floor_divide')(inputs, attr)
    return _impl

def _floormod():
    def _impl(inputs, attr, params, mod):
        assert len(inputs) == 2
        return AttrCvt('floor_mod')(inputs, attr)
    return _impl

def _logical(name):
    def _impl(inputs, attr, params, mod):
        return AttrCvt(op_name=name)(inputs, attr)
    return _impl

def _space_to_batch_nd():
    def _impl(inputs, attr, params, mod):
        input_node = inputs[0]
        input_shape = _infer_shape(input_node, mod)
        try:
            block_shape = _get_list_param(params, inputs[1])
        except (IndexError, KeyError, AttributeError):
            block_shape = _infer_value(inputs[1], params).asnumpy().tolist()

        try:
            paddings = _get_list_param(params, inputs[2])
        except (IndexError, KeyError, AttributeError):
            paddings = _infer_value(inputs[2], params).asnumpy()
            paddings = np.squeeze(paddings)
            if len(paddings.shape) == 1:
                paddings = np.expand_dims(paddings, exis=0)
            paddings = paddings.tolist()
        N = len(input_shape)
        M = len(block_shape)
        batch = input_shape[0]
        remaining_shape_length = N - M - 1
        paddings = [(0, 0)] + paddings + [(0, 0)] * remaining_shape_length
        # From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/space-to-batch-n-d:
        # Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings
        # to produce padded of shape padded_shape.
        padded = tvm.relay.nn.pad(input_node, pad_width=paddings)
        # Reshape padded to reshaped_padded of shape:
        # [batch] + [padded_shape[1] / block_shape[0], block_shape[0], ...,
        # padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape
        shape1 = [batch] + [item for i in range(M) for item in [-4, -1, block_shape[i]]] + [-2]
        reshaped_padded = tvm.relay.reshape(padded, newshape=shape1)
        # Permute dimensions of reshaped_padded to produce permuted_reshaped_padded of shape:
        # block_shape + [batch] + [padded_shape[1] / block_shape[0], ...,
        # padded_shape[M] / block_shape[M-1]] + remaining_shape
        axes = [2 * i + 2 for i in range(M)] + [0] + [2 * i + 1 for i in range(M)] + \
               list(range(1 + 2 * M, 1 + 2 * M + remaining_shape_length))
        permuted_reshaped_padded = tvm.relay.transpose(reshaped_padded, axes=axes)
        permuted_reshaped_padded_shape = _infer_shape(permuted_reshaped_padded)
        # Reshape permuted_reshaped_padded to flatten block_shape into the batch dimension,
        # producing an output tensor of shape:
        # [batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ...,
        # padded_shape[M] / block_shape[M-1]] + remaining_shape
        shape2 = [batch * np.prod(block_shape)] + list(permuted_reshaped_padded_shape)[M + 1:]
        reshaped_permuted_reshaped_padded = tvm.relay.reshape(permuted_reshaped_padded,
                                                              newshape=shape2)
        return reshaped_permuted_reshaped_padded

    return _impl


def _batch_to_space_nd():
    def _impl(inputs, attr, params, mod):
        input_node = inputs[0]
        input_shape = _infer_shape(input_node, mod)
        try:
            block_shape = _get_list_param(params, inputs[1])
        except (IndexError, KeyError, AttributeError):
            block_shape = _infer_value(inputs[1], params).asnumpy().tolist()

        try:
            crops = _get_list_param(params, inputs[2])
        except (IndexError, KeyError, AttributeError):
            crops = _infer_value(inputs[2], params).asnumpy()
            crops = np.squeeze(crops)
            if len(crops.shape) == 1:
                crops = np.expand_dims(crops, axis=0)
            crops = crops.tolist()
        M = len(block_shape)
        batch = input_shape[0]
        # From https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/batch-to-space-n-d:
        # Reshape input to reshaped of shape:
        # [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape),
        #  input_shape[1], ..., input_shape[N-1]]
        shape1 = block_shape + [batch // np.prod(block_shape)] + list(input_shape[1:])
        reshaped = tvm.relay.reshape(input_node, newshape=shape1)
        # Permute dimensions of reshaped to produce permuted of shape
        # [batch / prod(block_shape), input_shape[1], block_shape[0], ...,
        # input_shape[M], block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]]
        axes = [M] + [axis for i in range(M) for axis in [M + i + 1, i]] + \
            list(range(2 * M + 1, len(shape1)))
        permuted = tvm.relay.transpose(reshaped, axes=axes)
        # Reshape permuted to produce reshaped_permuted of shape
        # [batch / prod(block_shape), input_shape[1] * block_shape[0], ...,
        #  input_shape[M] * block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]]
        shape2 = [0] + [-3] * M + [-2]
        reshaped_permuted = tvm.relay.reshape(permuted, newshape=shape2)
        # Crop the start and end of dimensions [1, ..., M] of reshaped_permuted according to crops
        # to produce the output of shape:
        # [batch / prod(block_shape), input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1],
        #  ..., input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1],
        #  input_shape[M+1], ..., input_shape[N-1]]
        reshaped_permuted_shape = _infer_shape(reshaped_permuted)
        cropped = reshaped_permuted
        for axis in range(1, M+1):
            crop = crops[axis - 1]
            if crop != [0, 0]:
                indices = tvm.relay.arange(
                    _expr.const(crop[0]),
                    _expr.const(reshaped_permuted_shape[axis] - crop[1]),
                    dtype='int32'
                )
                cropped = tvm.relay.take(cropped, indices=indices, axis=axis)

        return cropped

    return _impl

def _atan2():
    def _impl(inputs, attr, params, mod):
        divide = _elemwise("divide")(inputs, attr, params, mod)
        return get_relay_op("atan")(divide)
    return _impl

def _prod():
    def _impl(inputs, attr, params, mod):
        axis = _get_num_param(params, inputs[1])
        keepdims = attr['keep_dims']
        return _op.prod(inputs[0], int(axis), keepdims=keepdims)
    return _impl

def _log1p():
    # op description: https://www.tensorflow.org/api_docs/python/tf/math/log1p
    def _impl(inputs, attr, params, mod):
        one = tvm.relay.const(1, attr['T'].name)
        add_out = get_relay_op('add')(inputs[0], one)
        return get_relay_op('log')(add_out)
    return _impl

def _one_hot():
    def _impl(inputs, attr, params, mod):
        depth = int(_get_num_param(params, inputs[1]))
        dtype = attr['T'].name

        on_value = _get_num_param(params, inputs[2])
        off_value = _get_num_param(params, inputs[3])
        new_inputs = [inputs[0],
                      tvm.relay.const(on_value, dtype),
                      tvm.relay.const(off_value, dtype)]
        return AttrCvt('one_hot',
                       ignores=['TI'],
                       extras={'depth' : depth, 'dtype' : dtype})(new_inputs, attr)
    return _impl

def _squared_difference():
    def _impl(inputs, attr, params, mod):
        difference = _op.subtract(inputs[0], inputs[1])
        return _op.multiply(difference, difference)
    return _impl

def _size():
    def _impl(inputs, attr, params, mod):
        new_attr = attr
        new_attr['out_type'] = attr['out_type'].name
        return AttrCvt('ndarray_size', transforms={'out_type' : 'dtype'})(inputs, new_attr)
    return _impl

def _add_n():
    def _impl(inputs, attr, params, mod):
        if not isinstance(inputs, tuple):
            inputs = list(inputs)
        assert len(inputs) > 0, "add_n take >=1 inputs, but 0 given."
        _res = inputs[0]
        for each in inputs[1:]:
            _res = _op.add(_res, each)
        return  _res
    return _impl


# compatible operators that do NOT require any conversion.
_identity_list = []

# Operators that get pruned away when the complete graph is frozen.
# These operators are not needed for inference.
_freezed_graph_pruned_op_list = ['ReadVariableOp', 'ResourceGather', 'Variable',
                                 'VariableV2', 'VarHandleOp', 'Assign', 'AssignVariableOp']


# _convert_map defines maps of name to converter functor(callable)
# for 1 to 1 mapping, use Renamer if nothing but name is different
# use AttrCvt if attributes need to be converted
# for 1 to N mapping(composed), use custom callable functions
# for N to 1 mapping, currently not supported(?)
_convert_map = {
    'Abs'                               : AttrCvt('abs'),
    'Add'                               : _elemwise('add'),
    'AddV2'                             : _elemwise('add'),
    'AddN'                              : _add_n(),
    'All'                               : _reduce('all'),
    'Any'                               : _reduce('any'),
    'ArgMax'                            : _argx(_op.argmax, 'argmax'),
    'ArgMin'                            : _argx(_op.argmin, 'argmin'),
    'Assert'                            : _assert(),
    'Atan'                              : AttrCvt('atan'),
    'Atan2'                             : _atan2(),
    'AvgPool'                           : _pooling('avg_pool'),
    'AvgPool3D'                         : _pool3d('avg_pool3d'),
    'BatchMatMul'                       : _batch_matmul(),
    'BatchMatMulV2'                     : _batch_matmul(),
    'BatchNormWithGlobalNormalization'  : _batch_norm(),
    'BatchToSpaceND'                    : _batch_to_space_nd(),
    'BiasAdd'                           : _bias_add(),
    'BroadcastTo'                       : _broadcast_to(),
    'Cast'                              : _cast(),
    'Ceil'                              : AttrCvt('ceil'),
    'CheckNumerics'                     : _check_numerics(),
    'ClipByValue'                       : _clip_by_value(),
    'Concat'                            : _concat(),
    'ConcatV2'                          : _concatV2(),
    'Conv2D'                            : _conv('conv'),
    'Conv3D'                            : _conv3d('conv'),
    'Conv2DBackpropInput'               : _conv('conv_transpose'),
    'CropAndResize'                     : _crop_and_resize(),
    'DecodeJpeg'                        : _decode_image(),
    'DepthwiseConv2dNative'             : _conv('depthwise'),
    'DepthToSpace'                      : _depth_to_space(),
    'Dilation2D'                        : _dilation2d(),
    'Equal'                             : _broadcast('equal'),
    'Elu'                               : _elu(),
    'Erf'                               : AttrCvt('erf'),
    'Exp'                               : AttrCvt('exp'),
    'ExpandDims'                        : _expand_dims(),
    'Fill'                              : _fill(),
    'Floor'                             : AttrCvt('floor'),
    'FloorDiv'                          : _floordiv(),
    'FloorMod'                          : _floormod(),
    'FusedBatchNorm'                    : _fused_batch_norm(),
    'FusedBatchNormV2'                  : _fused_batch_norm(),
    'FusedBatchNormV3'                  : _fused_batch_norm(),
    'Gather'                            : _gather(),
    'GatherNd'                          : _gather_nd(),
    'GatherV2'                          : _gather(),
    'Greater'                           : _broadcast('greater'),
    'GreaterEqual'                      : _broadcast('greater_equal'),
    'Identity'                          : _identity(),
    'IsFinite'                          : AttrCvt('isfinite'),
    'IsInf'                             : AttrCvt('isinf'),
    'LeakyRelu'                         : AttrCvt('leaky_relu'),
    'LeftShift'                         : AttrCvt('left_shift'),
    'Less'                              : _broadcast('less'),
    'LessEqual'                         : _broadcast('less_equal'),
    'Log'                               : AttrCvt('log'),
    'Log1p'                             : _log1p(),
    'Tan'                               : AttrCvt('tan'),
    'Cos'                               : AttrCvt('cos'),
    'Sin'                               : AttrCvt('sin'),
    'LogicalAnd'                        : _logical('logical_and'),
    'LogicalOr'                         : _logical('logical_or'),
    'LogicalNot'                        : _logical('logical_not'),
    'LogSoftmax'                        : AttrCvt('log_softmax'),
    'LRN'                               : _lrn(),
    'MatMul'                            : _matmul(),
    'Max'                               : _reduce('max'),
    'MaxPool'                           : _pooling('max_pool'),
    'MaxPool3D'                         : _pool3d('max_pool3d'),
    'Maximum'                           : _elemwise('maximum'),
    'Mean'                              : _mean(),
    'Min'                               : _reduce('min'),
    'Minimum'                           : _elemwise('minimum'),
    'MirrorPad'                         : _mirror_pad(),
    'Mod'                               : _elemwise('mod'),
    'Mul'                               : _elemwise('multiply'),
    'Neg'                               : AttrCvt('negative'),
    'NoOp'                              : _no_op(),
    'NotEqual'                          : _broadcast('not_equal'),
    'OneHot'                            : _one_hot(),
    'Pack'                              : _pack(),
    'TensorArrayV3'                     : _tensor_array(),
    'TensorArrayScatterV3'              : _tensor_array_scatter(),
    'TensorArrayGatherV3'               : _tensor_array_gather(),
    'TensorArraySizeV3'                 : _tensor_array_size(),
    'TensorArrayWriteV3'                : _tensor_array_write(),
    'TensorArrayReadV3'                 : _tensor_array_read(),
    'TensorArraySplitV3'                : _tensor_array_split(),
    'TensorArrayConcatV3'               : _tensor_array_concat(),
    'Pad'                               : _pad('Pad'),
    'PadV2'                             : _pad('PadV2'),
    'Pow'                               : _elemwise('power'),
    'Prod'                              : _prod(),
    'Range'                             : _range(),
    'Rank'                              : _rank(),
    'RealDiv'                           : _elemwise('divide'),
    'Relu'                              : AttrCvt('relu'),
    'Relu6'                             : _relu6(),
    'Reshape'                           : _reshape(),
    'ResizeBilinear'                    : _resize('bilinear'),
    'ResizeBicubic'                     : _resize('bilinear'),
    'ResizeNearestNeighbor'             : _resize('nearest_neighbor'),
    'ReverseV2'                         : _reverse_v2(),
    'RightShift'                        : AttrCvt('right_shift'),
    'Round'                             : AttrCvt('round'),
    'Rsqrt'                             : _rsqrt(),
    'Select'                            : _where(),
    'Selu'                              : _selu(),
    'Shape'                             : _shape(),
    'Sigmoid'                           : AttrCvt('sigmoid'),
    'Sign'                              : AttrCvt('sign'),
    'Size'                              : _size(),
    'Slice'                             : _slice(),
    'Softmax'                           : _softmax(),
    'Softplus'                          : _softplus(),
    'SpaceToBatchND'                    : _space_to_batch_nd(),
    'SpaceToDepth'                      : _space_to_depth(),
    'Split'                             : _split(False),
    'SplitV'                            : _split(True),
    'Sqrt'                              : AttrCvt('sqrt'),
    'Square'                            : _square(),
    'SquaredDifference'                 : _squared_difference(),
    'Squeeze'                           : _squeeze(),
    'StopGradient'                      : _identity(),
    'StridedSlice'                      : _stridedSlice(),
    'Sub'                               : _elemwise('subtract'),
    'Sum'                               : _sum(),
    'Tanh'                              : AttrCvt('tanh'),
    'Tile'                              : _tile(),
    'TopKV2'                            : _topk(),
    'Transpose'                         : _transpose(),
    'TruncateMod'                       : _elemwise('mod'),
    'Unpack'                            : _unpack(),
    'UnravelIndex'                      : _unravel_index(),
    'Where'                             : _where(),
    'ZerosLike'                         : AttrCvt('zeros_like'),

}

def _LSTMBlockCell():
    def _impl(inputs, in_state_c, in_state_h, attr, params, mod):
        """LSTM Block cell.
        Calculations are described in: https://github.com/tensorflow/tensorflow/blob/
        r1.8/tensorflow/contrib/rnn/python/ops/lstm_ops.py#L41-L114

        Parameters
        ----------
        inputs : relay.Expr
            Input data
        in_state_c: list of relay.Expr
            Cell state input values for all the layers
        in_state_h: list of relay.Expr
            Hidden state input values for all the layers
        attrs : dict
            Dict of operator attributes
        params : dict
            List of pretrained weights and bias

        Returns
        -------
        sym : relay.Expr
            Converted relay.Expr
        output: relay.Expr
            Output state value.
        """
        in_data = inputs[0]
        in_weight = inputs[3]
        in_bias = inputs[7]
        forget_bias = attr.pop('forget_bias')
        input_shape = _infer_shape(inputs[0], mod)
        weight_shape = _infer_shape(inputs[3], mod)
        batch_size, input_size = input_shape[0], input_shape[1]
        num_hidden_layers = weight_shape[1]
        num_hidden = num_hidden_layers // 4

        in_data = _op.reshape(in_data,
                              newshape=(batch_size, input_size))
        ixh = _op.concatenate([in_data, in_state_h], axis=1)
        in_weight = _op.transpose(in_weight, axes=None)
        gates = _op.nn.dense(ixh, in_weight,
                             units=num_hidden_layers)
        gates_bias = _op.add(gates, in_bias)
        gate_list = _op.split(gates_bias, indices_or_sections=4, axis=1)
        in_gate = _op.sigmoid(gate_list[0])
        in_transform = _op.tanh(gate_list[1])
        forget_gate = _op.add(gate_list[2], tvm.relay.const(forget_bias, attr['T'].name))
        forget_gate = _op.sigmoid(forget_gate)
        out_gate = _op.sigmoid(gate_list[3])
        next_c = _op.add(_op.multiply(forget_gate, in_state_c),
                         _op.multiply(in_gate, in_transform))
        next_h = out_gate * _op.tanh(next_c)
        out_state = _op.concatenate([next_c, next_h], axis=1)
        out_state = _op.reshape(out_state,
                                newshape=(2, batch_size, num_hidden))
        return next_h, out_state
    return _impl

# _convert_map_rnn defines maps of rnn operator name to
# converter functor(callable) for 1 to 1 mapping.
_convert_map_rnn = {
    'LSTMBlockCell'                     : _LSTMBlockCell(),
}

class RecurrentNetworks(object):
    """Recurrent network layer handlers.

    Handle Layer operations.
    ToDo: Operators like RNN/GRU layer concepts also can be handled here

    Parameters
    ----------
    nodes : list
        list of graph nodes used for tensorflow parsing.

    out_rnn : list
        List of RecurrentNetwork outputs. This output will be appended to the
        'head' nodes of the graph.

    graph : tensorflow graph definition object
        The loaded tensorflow GraphDef

    convert_map : dict
        Dict of name : callable, where name is the op's name that
        require conversion to relay, callable are functions which
        take attrs and return (new_op_name, new_attrs)
    """
    def __init__(self, nodes, out_rnn, graph, convert_map):
        self._graph = graph
        self._convert_map = convert_map
        self._nodes = nodes
        self._out_rnn = out_rnn
        self._cur_lstm_layer = 0
        self._layer_name_list = []
        self._recurrent_ops_layer_map = {
            'LSTMBlockCell'               : self._LSTMBlockCellLayer(),
        }

    def _LSTMBlockCellLayer(self):
        """LSTMBlockCell layer handler.

        Parameters
        ----------
        op_name : str
            Operator name, eg:LSTMBlockCell

        layer_name : str list
            Layer name is used for creating the state input placeholder.

        inputs : relay.Expr
            Input data

        attrs : dict
            Dict of operator attributes

        params : dict
            List of pretrained weights and bias

        num_layers : int
            Total number of LSTM layer presented in the graph

        Returns
        -------
        sym : relay.Expr
            The returned relay Expr
        """
        def _impl(op_name, layer_name, inputs, attrs, params, num_layers, mod):
            in_state_c_name = layer_name+'_c'
            in_state_h_name = layer_name+'_h'

            def _init_state(num_layers, batch_size, num_hidden):
                """Create the initial states for the first layer in the graph."""
                in_state_c = [_expr.var(in_state_c_name,
                                        shape=(num_layers, batch_size, num_hidden),
                                        dtype='float32')]

                in_state_h = [_expr.var(in_state_h_name,
                                        shape=(num_layers, batch_size, num_hidden),
                                        dtype='float32')]
                return in_state_c, in_state_h

            def _get_cur_input_state(in_state_c, in_state_h, num_layers,
                                     layer, batch_size, num_hidden):
                """Select the appropriate states for the current layer"""
                in_state_c_tup = _op.split(in_state_c[0],
                                           indices_or_sections=num_layers, axis=0)
                in_state_h_tup = _op.split(in_state_h[0],
                                           indices_or_sections=num_layers, axis=0)
                cur_in_state_c = _op.reshape(in_state_c_tup[layer],
                                             newshape=(batch_size, num_hidden))
                cur_in_state_h = _op.reshape(in_state_h_tup[layer],
                                             newshape=(batch_size, num_hidden))
                return cur_in_state_c, cur_in_state_h

            def _LSTMBlockCellWrapper(inputs, attr, params,
                                      num_layers, layer):
                """LSTM cell warapper to prepare the inputs"""
                input_shape = _infer_shape(inputs[0], mod)
                weight_shape = _infer_shape(inputs[3], mod)

                batch_size = input_shape[0]
                num_hidden = weight_shape[1] // 4

                if layer == 0:
                    #Create initial states placeholder in case of first layer
                    in_state_c, in_state_h = _init_state(num_layers,
                                                         batch_size, num_hidden)
                else:
                    in_state_c = self._nodes[in_state_c_name]
                    in_state_h = self._nodes[in_state_h_name]

                cur_in_state_c, cur_in_state_h = _get_cur_input_state(
                    in_state_c, in_state_h,
                    num_layers, layer,
                    batch_size, num_hidden)
                output, out_state = self._convert_map[op_name](inputs, cur_in_state_c,
                                                               cur_in_state_h,
                                                               attr, params, mod)
                return output, out_state, in_state_c, in_state_h

            sym, cur_out_state, in_state_c, in_state_h = \
                    _LSTMBlockCellWrapper(inputs, attrs, params,
                                          num_layers, self._cur_lstm_layer)
            self._nodes[in_state_c_name] = in_state_c
            self._nodes[in_state_h_name] = in_state_h
            cur_out_state = _op.expand_dims(cur_out_state, axis=0, num_newaxis=1)
            self._out_rnn.append(cur_out_state)
            self._cur_lstm_layer += 1
            return sym
        return _impl

    def process_op(self, op_name, inputs, attrs, params, mod):
        """Process recurrent layer operators.

        List '_recurrent_ops_layer_map' map each Layer based operators with its
        layer handlers. Total number of layers are calculated to form the input
        data shapes.

        Parameters
        ----------
        op_name : str
            Operator name, such as LSTMBlockCell

        inputs : relay.Expr
            Input data

        attrs : dict
            Dict of operator attributes

        params : dict
            List of pretrained weights and bias

        Returns
        -------
        sym : relay.Expr
            Returns relay.Expr
        """
        def _get_abs_layer_name(node):
            """Identify the layer name is already handled. Return the absolute name
            """
            if not self._layer_name_list:
                self._layer_name_list.append(node.name)
                return node.name

            for _name in self._layer_name_list:
                if _name in node.name:
                    abs_name = _name
                else:
                    self._layer_name_list.append(node.name)
                    abs_name = node.name
            return abs_name

        #Find number of layers of this same operator node in the graph
        #and also read the inputs name for the current op.
        num_layers = 0
        for _, node in enumerate(self._graph.node):
            if node.op == op_name:
                layer_name = _get_abs_layer_name(node)
                num_layers += 1

        sym = self._recurrent_ops_layer_map[op_name](op_name, layer_name, inputs, attrs,
                                                     params, num_layers, mod)
        return sym

# An internal list to contain all the control flow primitives used in Tensorflow
# 1.x.
_control_flow_nodes = ['Merge', 'Switch', 'NextIteration', 'Exit', 'Enter', 'LoopCond']

class RewriteSubgraph(ExprMutator):
    """
    A helper class to rewrite expr in while loop function to variable

    Parameters
    ----------
    rewrite_map : Dict[expr, expr]
        A dictionay contains a set of expr to var mapping.
    """
    def __init__(self, rewrite_map):
        ExprMutator.__init__(self)
        self.rewrite_map = rewrite_map

    def visit(self, expr):
        if expr in self.rewrite_map:
            return self.rewrite_map[expr]
        return super().visit(expr)

def rewrite_subgraph(expr, rewrites):
    return RewriteSubgraph(rewrites).visit(expr)

def _in_while_loop(control_flow_node_map, op_name):
    """
    Check if a given control flow operator is part of a while loop execution
    frame. This is based on the fact that there is only one occurrence of
    `LoopCond` for a loop execution frame and it is only presented in the loop
    construct.

    Parameters
    ----------
    control_flow_node_map : Dict[str, Set[str]]
        A dictionay contains the unique control flow execution frame name to
        a set of primitive operators mapping.

    op_name : str
        The name of a control flow primitive.

    Returns
    -------
    ret : bool
        Return true if the operator is in a while loop execution frame,
    otherwise, return false.
    """
    return op_name in control_flow_node_map and \
            "LoopCond" in control_flow_node_map[op_name]

class Branch:
    """A class contains the components that are used to build up a Relay if
    node.

    Parameters
    ----------
    cond : tvm.relay.Expr
        The condition of a if node.

    true_branch : tvm.relay.Expr
        The body of the true branch of a if expression.

    false_branch: tvm.relay.Expr
        The body of the false branch of a if expression.

    _if : tvm.relay.Expr
        An internal variable indicates where an if expression is already created
        for a matched TF condition construct.

    Examples
    --------
    The following is a cond statement written in TensorFlow:

    .. code-block:: python

        def vanilla_cond():
            i = tf.constant(1)
            j = tf.constant(4)

             def f1():
                return tf.multiply(1, 17)

             def f2():
                return tf.add(4, 23)
            r = tf.cond(tf.less(i, j), f1, f2)

    This condition statement should be converted into Relay in the following
    form:

    .. code-block:: python

        fn (%Const: Tensor[(1,), int32],
            %Const_1: Tensor[(1,), int32],
            %cond/Mul/x: Tensor[(1,), int32],
            %cond/Mul/y: Tensor[(1,), int32],
            %cond/Add/x: Tensor[(1,), int32],
            %cond/Add/y: Tensor[(1,), int32]) {
          %0 = less(%Const, %Const_1) # ty=Tensor[(1,), bool]
          %1 = min(%0)
          if (%1) {
            %2 = multiply(%cond/Mul/x, %cond/Mul/y)
            %2
          }  else {
            %3 = add(%cond/Add/x, %cond/Add/y)
            %3
          }
        }
    """
    def __init__(self):
        self._if = None
        self.cond = None
        self.true_branch = None
        self.false_branch = None

    def _if_node(self):
        """An internal API to create a relay if node from the matched TF
        condition construct.
        """
        # `cond`  returns a tensor that contains boolean values. We add a `min`
        # operator to checks if there is any false value. If so, this condition
        # doesn't not hold.
        cond = tvm.relay.op.min(self.cond)
        return tvm.relay.If(cond, self.true_branch, self.false_branch)

    def if_node(self):
        """Create an tvm.relay.If node if it hasn't been created yet."""
        if self._if is None:
            self._if = self._if_node()
        return self._if


class LoopBound(ExprVisitor):
    """
    When a loop body is create, we get a Relay expression backtracing all
    the way back to input node. This will result in lots of unnecessary
    expression placed into loop body and compute multiple times. For example,
    consider the following tensorflow code:

    .. code-block:: python

        i = tf.constant(0)
        data = tf.compat.v1.placeholder(tf.float32, shape=(1024, 1024))
        slice = tf.strided_slice(data, 0, 512)
        def c(i): return tf.less(i, 10)
        def b(i): return [tf.add(i, 1), tf.add(i, 1) + slice]
        r = tf.while_loop(c, b, [i])

    If we directly create recursive function, slice will be placed into function body.
    Instead, we recognize whether slice is inside while_loop block and pass it as an
    extra loop variable to avoid duplicate computation.

    TODO(kevinthesun): Add a LICM pass for Relay to handle generic loop/function.
    """
    def __init__(self, loop_name, hash2tfnode, while_loop_name_set):
        ExprVisitor.__init__(self)
        self._loop_name = loop_name
        self._hash2tfnode = hash2tfnode
        self._while_loop_name_set = while_loop_name_set
        self.extra_loop_var_names = set()

    def _find_parent_loop_name(self, node_name):
        """Find name of direct parent while loop."""
        ploop_name = ""
        name_prefix = node_name.rsplit('/', 1)[0]
        if name_prefix.startswith("^"):
            name_prefix = name_prefix[1:]
        # To get the name of the direct parent while loop for a given node,
        # we iterate all the while loop names inside TensorFlow graph def.
        # If we find a loop name with which current node name starts,
        # it means current node is under this loop. However, due to nested
        # loop, this loop may not be the direct parent while loop of current
        # node. We need to keep the longest loop name, which represents the
        # innermost while loop corresponding to current node.
        for lname in self._while_loop_name_set:
            if name_prefix.startswith(lname) and len(ploop_name) < len(lname):
                ploop_name = lname

        if len(ploop_name) == 0:
            ploop_name = name_prefix

        return ploop_name

    def visit(self, expr):
        """
        For each expression in the body, look up the corresponding
        TensorFlow node with its structural hash. If the current loop is the
        direct parent of this node, we check whether its every input node belongs
        to the current loop. If not, we mark this input node as an extra loop
        variable to the current loop.
        """
        expr_hash = s_hash(expr)

        if expr_hash in self._hash2tfnode:
            node = self._hash2tfnode[expr_hash]
            ploop_name = self._find_parent_loop_name(node.name)
            # It is possibel that a node is under nested loop of current loop.
            # We only check the direct children of current loop.
            if ploop_name == self._loop_name:
                for iname in node.input:
                    iploop_name = self._find_parent_loop_name(iname)
                    # Use startswith to deal with nested loop
                    if not iploop_name.startswith(self._loop_name):
                        if iname not in self.extra_loop_var_names:
                            self.extra_loop_var_names.add(iname)
        super().visit(expr)


class Loop:
    """
    A class contains the components that are used to build up a Relay
    recursive call.

    Parameters
    ----------
    loop_vars : List[tvm.relay.Expr]
        The loop variables that used in a while loop.

    cond : tvm.relay.Expr
        The condition of a while loop.

    body : tvm.relay.Expr
        The body of a matched while loop.

    _loop : tvm.relay.Expr
        An internal variable indicates where a recursive call is already created
        for a matched TF while loop construct.

    Examples
    --------
    The following is a vanilla loop from TensorFlow:

    .. code-block:: python

        i = tf.constant(0)
        c = lambda i: tf.less(i, 10)
        b = lambda i: tf.add(i, 1)
        r = tf.while_loop(c, b, [i])

    It will be converted to the following recursive call in Relay:

    .. code-block:: python

        fn (%while/Less/y: Tensor[(1,), int32],
            %while/Add/y: Tensor[(1,), int32],
            %Const: Tensor[(1,), int32]) {
          %0 = fn(%loop_var0: Tensor[(1,), int32]) {
            %1 = less(%loop_var0, %while/Less/y)
            %2 = min(%1)
            if (%2) {
              %3 = add(%loop_var0, %while/Add/y)
              free_var %while_loop
              %4 = %while_loop(%3)
              %4
            }    else {
              %5 = (%loop_var0,)
              %5
            }
          }
          let %while_loop1 = %0
          %6 = %while_loop1(%Const)
          %6
        }
    """
    def __init__(self, mod, loop_name, hash2tfnode,
                 node_map, while_loop_name_set):
        self.loop_vars = []
        self.cond = None
        self.body = []
        self._loop = None
        self._mod = mod
        self._loop_name = loop_name
        self._hash2tfnode = hash2tfnode
        self._node_map = node_map
        self._while_loop_name_set = while_loop_name_set
        self.aligned = False

    def _while_loop(self):
        """An internal API to create a Relay recursive call for a matched TF
        `while_loop` construct.
        """
        wl = tvm.relay.var('while_loop')

        sb = tvm.relay.scope_builder.ScopeBuilder()

        loop_checker = LoopBound(self._loop_name,
                                 self._hash2tfnode,
                                 self._while_loop_name_set)
        for body in self.body:
            loop_checker.visit(body)

        loop_vars = []
        bind_map = {}
        loop_var_hash_set = set()
        for var in self.loop_vars:
            loop_var_hash_set.add(s_hash(var))

        extra_nodes = []
        for extra_loop_var_name in loop_checker.extra_loop_var_names:
            extra_loop_var_name = extra_loop_var_name.split(':')[0].split("^")[-1]
            extra_node = self._node_map[extra_loop_var_name]
            extra_node = extra_node if isinstance(extra_node, _expr.Tuple) else extra_node[0]
            if s_hash(extra_node) not in loop_var_hash_set:
                self.loop_vars.append(extra_node)
                extra_nodes.append(extra_node)

        for i, var in enumerate(self.loop_vars):
            if not isinstance(var, _expr.Var):
                var_chk = _infer_type(var, self._mod)
                var_type = var_chk.checked_type
            else:
                var_type = var.type_annotation

            v = tvm.relay.var("loop_var" + str(i), type_annotation=var_type)
            loop_vars.append(v)
            bind_map[var] = v


        self.cond = rewrite_subgraph(self.cond, bind_map)
        self.body = [rewrite_subgraph(b, bind_map) for b in self.body]

        self.body_shape = []
        for body in self.body:
            current_node = body
            shape = _infer_shape(current_node, self._mod)
            while not isinstance(shape, (tuple, list)):
                current_node = current_node.args[-1]
                shape = _infer_shape(current_node, self._mod)
            self.body_shape.append(shape)

        cond = tvm.relay.op.min(self.cond)

        with sb.if_scope(cond):
            extra_args = []
            if extra_nodes:
                extra_args = list(loop_vars[-len(extra_nodes):])
            sb.ret(wl(*list(self.body + extra_args)))
        with sb.else_scope():
            sb.ret(tvm.relay.Tuple(loop_vars))

        loop_fn = tvm.relay.Function(loop_vars, sb.get())
        sb = tvm.relay.scope_builder.ScopeBuilder()
        sb.let(wl, loop_fn)
        loop_ret = wl(*self.loop_vars)

        sb.ret(loop_ret)
        ret = sb.get()
        return ret

    def while_loop(self):
        """Instantiate a while loop if it has not been created yet."""
        if self._loop is None:
            self._loop = self._while_loop()
            return self._loop
        return self._loop


class GraphProto(object):
    """ A helper class for handling relay graph copying from Tensorflow GraphDef.
    Definition:
        https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto
    """
    def __init__(self):
        self._nodes = {}
        self._tf_node_map = {}
        self._params = {}
        self._input_shapes = {}
        self._output_shapes = {}
        self._num_rnn_layer = False
        self._input_shapes = {}
        self._loops = {}
        self._branches = {}
        self._mod = IRModule({})
        self._prelude = Prelude(self._mod)
        self._control_flow_node_map = defaultdict(set)
        self._loop_body_order = {}
        self._loop_var_order = {}
        self._hash2tfnode = {}
        self._while_loop_name_set = set()

    def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
        """Construct relay nodes from tensorflow graph definition - GraphDef.

        Follow the tensorflow graph definition to parse and convert it to Relay.
        Some of the assumptions listed below.

            -> All Placeholders are considered as graph input.
            -> All Const nodes are params.
            -> Last node is assumed as graph output.
            -> _output_shapes : Graph should be frozen with add_shapes=True.
                                Or user can pass input shape dictionary optionally.
            -> DecodeJpeg, ResizeBilinear: These are dummy operators.
                                           Hence user should handle preprocessing outside.
            -> CheckNumerics: No implementation as of now for this.
                              Just copies input to output.

        Parameters
        ----------
        graph : tensorflow graph definition object
            The loaded tensorflow GraphDef

        layout : target layout to be used (Optional)
            NCHW only supported now to enable NHWC models on GPU.

        shape : Dictionary of input dimensions (Optional)
            Graph level input shape dictionary.

        outputs : List of output tensor names (Optional)
            if not specified then the last node is assumed as graph output.

        Returns
        -------
        mod : tvm.IRModule
            The module that optimizations will be performed on.

        params : dict
            A dict of name: tvm.nd.array pairs, used as pretrained weights
        """
        try:
            from tensorflow.python.framework import tensor_util
        except ImportError as e:
            raise ImportError(
                "Unable to import tensorflow which is required {}".format(e))

        missing_operators = self._parse_import_prerequisites(graph)
        control_flow_nodes = []
        self._in_shape = shape
        self._layout = layout
        self._graph = graph

        if missing_operators:
            freezed_ops = [op for op in missing_operators if op in _freezed_graph_pruned_op_list]
            if freezed_ops:
                raise Exception("Graph is not frozen. Provide a frozen graph. "
                                "Found operators {}".format(freezed_ops))

            raise NotImplementedError(
                "The following operators are not implemented: {}".format(missing_operators))

        for node in graph.node:
            node_name_prefix = node.name.rsplit('/', 1)[0]
            self._control_flow_node_map[node_name_prefix].add(node.op)
            self._tf_node_map[node.name] = node

            # Parse output_shapes attribute
            parsed_attr = self._parse_attr(node.attr)
            if '_output_shapes' in parsed_attr:
                self._output_shapes[node.name] = \
                    [tensor_util.TensorShapeProtoToList(tshape) \
                     for tshape in parsed_attr['_output_shapes']]
            else:
                self._output_shapes[node.name] = [None]

            # Parse placeholder and const here since input shape info is required.
            if node.op == 'Placeholder' or node.op == 'PlaceholderWithDefault':
                # Give priority to user argument.
                if shape and node.name in shape:
                    self._input_shapes[node.name] = list(shape[node.name])
                else:
                    self._input_shapes[node.name] = \
                        tensor_util.TensorShapeProtoToList(node.attr['shape'].shape)
                    for idx, dim in enumerate(self._input_shapes[node.name]):
                        if dim < 0:
                            self._input_shapes[node.name][idx] = 1
                            warnings.warn("Use 1 instead of -1 in shape of operator %s."
                                          % node.name)

                self._output_shapes[node.name] = [self._input_shapes[node.name]]
                attr = self._parse_attr(node.attr)
                self._nodes[node.name] = [_expr.var(node.name,
                                                    shape=self._input_shapes[node.name],
                                                    dtype=attr['dtype'].name)]

                # Ignore user's input shape for Non placeholder
            elif node.op == 'Const':
                tensor_value = node.attr['value'].tensor
                self._input_shapes[node.name] = \
                    tensor_util.TensorShapeProtoToList(tensor_value.tensor_shape)
                self._output_shapes[node.name] = [self._input_shapes[node.name]]
                if shape and node.name in shape:
                    warnings.warn("Ignore the passed shape. Shape in graphdef "
                                  "will be used for operator %s." % node.name)
                for key, value in node.attr.items():
                    self._parse_param(key, value, node.name, self._in_shape)
            elif node.op in _control_flow_nodes:
                # We assume that the direct parent node of Exit is a while loop block
                if node.op == "Exit":
                    self._while_loop_name_set.add(node_name_prefix)
                control_flow_nodes.append(node)

        # First, parse all control flow nodes.
        # Convert tf.cond to Branch and tf.while_loop to Loop.
        sorted_cf_nodes = []
        current_node_name_prefix = None
        exits = []
        # Sort control flow nodes to move all Exit nodes to the end
        # of corresponding while_loop block.
        for i, node in enumerate(control_flow_nodes):
            node_name_prefix = node.name.rsplit('/', 1)[0]
            if current_node_name_prefix is None or current_node_name_prefix != node_name_prefix:
                if node_name_prefix in self._while_loop_name_set:
                    sorted_cf_nodes.extend(exits)
                    exits.clear()
                    current_node_name_prefix = node_name_prefix

            if node.op == "Exit":
                exits.append(node)
            else:
                sorted_cf_nodes.append(node)

            if i == len(control_flow_nodes) - 1:
                sorted_cf_nodes.extend(exits)

        for node in sorted_cf_nodes:
            self._backtrack_construct(node.name)

        # Second, parse other nodes to re-create TF graph using Relay operators.
        for node in graph.node:
            self._backtrack_construct(node.name)

        out = []
        if outputs is None:
            last_node = graph.node[-1]
            op = self._nodes[last_node.name.split(":")[0]]
            if last_node.op == "Exit":
                out = [op[0].tuple_value]
            else:
                out = op
        else:
            for out_name in outputs:
                if ":" in out_name:
                    out_name, out_num = out_name.split(":")
                    out_num = int(out_num)
                    out.append(self._nodes[out_name][out_num])
                else:
                    out.append(self._nodes[out_name][0])

        #Add the RNN outputs also with 'head' nodes of the relay graph
        if self._num_rnn_layer:
            if len(self._out_rnn) == 1:
                out.append(self._out_rnn[0])
            else:
                out_rnn = _op.concatenate(self._out_rnn, axis=0)
                out.append(out_rnn)

        out = out[0] if len(out) == 1 else _expr.Tuple(out)
        func = _function.Function(analysis.free_vars(out), out)
        self._mod["main"] = func
        return self._mod, self._params

    def _parse_import_prerequisites(self, graph):
        """ Calculate the named preconditions from TensorFlow `graph`.
            Return prerequisites for parsing:
            a. Set of operator names which don't have their mapping in TVM, i.e.
                which are not supported
        """
        missing_operators = set()
        for node in graph.node:
            if node.op == "Placeholder" or node.op == 'PlaceholderWithDefault':
                pass
            elif node.op == "Const":
                pass
            else:
                if any([node.op in t for t in [_identity_list, _convert_map,
                                               _convert_map_rnn,
                                               _control_flow_nodes]]):
                    pass
                else:
                    missing_operators.add(node.op)

        return missing_operators

    def _parse_param(self, key, value, name, shape):
        try:
            from tensorflow.python.framework import tensor_util
        except ImportError as e:
            raise ImportError(
                "Unable to import tensorflow which is required {}".format(e))

        if key == 'value':
            np_array = tensor_util.MakeNdarray(value.tensor)

            if np_array.dtype == np.dtype(object):
                # Object types are generally tensorflow DT_STRING (DecodeJpeg op).
                # Just leave it as placeholder.
                if shape and name in shape:
                    var_shape = shape[name]
                else:
                    var_shape = tensor_util.TensorShapeProtoToList(value.tensor.tensor_shape)
                self._nodes[name] = [_expr.var(name, shape=var_shape, dtype='uint8')]
                return

            array_ndim = len(np_array.shape)
            if array_ndim == 0:
                self._nodes[name] = [tvm.relay.const(np_array)]
            else:
                self._params[name] = tvm.nd.array(np_array)
                self._nodes[name] = [_expr.var(name,
                                               shape=self._params[name].shape,
                                               dtype=self._params[name].dtype)]
        else:
            if key not in ('dtype', '_output_shapes', '_class'):
                raise NotImplementedError \
                    ("Other attributes for a Const(param) Node {} ? .".format(key))

    def _get_attr(self, buf):
        """Returns the value of the attr of this buf with the given `name`.

        Args:
          buf: attrvalue protobuf.

        Returns:
          The value of the attr, as a Python object.

        Raises:
          ValueError: If this op does not have an attr with the given `name`.
        """
        fields = ["s", "i", "f", "b", "type", "shape", "tensor", "func"]

        x = buf

        ret = []

        try:
            from tensorflow.python.framework import dtypes
        except ImportError as e:
            raise ImportError(
                "Unable to import tensorflow which is required {}".format(e))

        # Treat an empty oneof value as an empty list.
        if not x.WhichOneof("value"):
            return ret
        if x.HasField("list"):
            for f in fields:
                if getattr(x.list, f):
                    if f == "type":
                        ret += [dtypes.as_dtype(x) for x in list(getattr(x.list, f))]
                    else:
                        ret += list(getattr(x.list, f))
        else:
            for f in fields:
                if x.HasField(f):
                    if f == "type":
                        ret = dtypes.as_dtype(getattr(x, f))
                    else:
                        ret = getattr(x, f)
        return ret

    def _parse_attr(self, attr_proto):
        """Convert a list of AttributeProto to a dict, with names as keys."""
        attrs = {}
        for key, value in attr_proto.items():
            attrs[key] = self._get_attr(value)

        return attrs

    def _convert_rnn_operator(self, op_name, inputs,
                              attrs, params, graph, convert_map):
        """Convert RNN and its variant operators to Relay operators.
        This converter read the input states of each layers and
        also maintain the output states of each layer in a list.

        Parameters
        ----------
        op_name : str
            Operator name, such as LSTMBlockCell
        inputs : list of relay.Expr
            List of input symbols.
        attrs : dict
            Dict of operator attributes
        params : dict
            List of pretrained weights and bias
        graph : Tensorflow graph object
            Graph is to find the number of upcoming same operator to
            calculate the number of layers.
        convert_map : dict
            Dict of name : callable, where name is the op's name that
            require conversion to relay, callable are functions which
            take attrs and return (new_op_name, new_attrs)

        Returns
        -------
        sym : relay.Expr
            Converted relay.Expr
        """
        if not self._num_rnn_layer:
            self._out_rnn = []
            self.rnn = RecurrentNetworks(self._nodes, self._out_rnn, graph, convert_map)
            self._num_rnn_layer = True
        sym = self.rnn.process_op(op_name, inputs, attrs, params, self._mod)
        return sym

    def _convert_control_flow_operator(self, node, inputs, attrs, control_flow_node_map):
        """
        Convert the Relay control flow primitive into corresponding component
        of a Relay control flow construct, i.e. `tf.cond` and `tf.while_loop`
        are converted in Relay `If` and recusrive call, respectively.

        Parameters
        ----------
        node: TensorFlow graph node object.
            A TensorFlow graph node object.

        inputs : List[tvm.relay.Expr]
            List of input symbols.

        attrs : Dict[tvm.Attrs]
            Dict of operator attributes.

        control_flow_node_map : Dict[str, Set[str]]
            A dictionary contains the execution frame name to primitives
            mapping.

        Returns
        -------
        op : tvm.relay.Expr
            Converted relay expression.
        """
        node_name_prefix = node.name.rsplit('/', 1)[0]
        if node.op == "Merge":
            if _in_while_loop(self._control_flow_node_map, node_name_prefix):
                op = self._backtrack_construct(node.input[0])
                if node_name_prefix not in self._loops:
                    self._loops[node_name_prefix] = Loop(self._mod,
                                                         node_name_prefix,
                                                         self._hash2tfnode,
                                                         self._nodes,
                                                         self._while_loop_name_set)
            else:
                if len(self._branches) == 0:
                    raise RuntimeError("Cannot find a created "
                                       "conditional for merge node")
                branch = self._branches[node_name_prefix]
                false_br = self._backtrack_construct(node.input[0])
                true_br = self._backtrack_construct(node.input[1])
                assert len(true_br) == 1
                assert len(false_br) == 1
                branch.true_branch = true_br[0]
                branch.false_branch = false_br[0]
                op = [branch.if_node()]
                if node_name_prefix not in self._while_loop_name_set:
                    try:
                        cond_val = np.all(_infer_value(branch.cond, self._params,
                                                       self._mod).asnumpy())
                        if cond_val:
                            op = [branch.true_branch]
                        else:
                            op = [branch.false_branch]
                    except Exception:
                        op = [branch.if_node()]
        elif node.op == "Exit":
            loop = self._loops[node_name_prefix]

            # Check whether the order of loop variables aligns
            # with loop body. If not, create new loop variable list
            # with correct order.
            if not loop.aligned:
                loop_vars = []
                for i in self._loop_body_order[node_name_prefix]:
                    for j, k in enumerate(self._loop_var_order[node_name_prefix]):
                        if k == i:
                            loop_vars.append(loop.loop_vars[j])
                loop.loop_vars = loop_vars
                loop.aligned = True
            exit_name = node.name.split('/')[-1]
            if '_' in exit_name:
                exit_number = int(exit_name[5:])
            else:
                exit_number = 0
            expr = loop.while_loop()
            body_pos = exit_number
            for i, j in enumerate(self._loop_body_order[node_name_prefix]):
                if exit_number == j:
                    body_pos = i
                    break
            op = [_expr.TupleGetItem(expr, body_pos)]
        elif node.op == "Enter":
            op = self._backtrack_construct(node.input[0])
        elif node.op == "LoopCond":
            op = self._backtrack_construct(node.input[0])
            assert len(op) == 1
            self._loops[node_name_prefix].cond = op[0]
        elif node.op == "Switch":
            op = self._backtrack_construct(node.input[0])
            cond = self._backtrack_construct(node.input[1])
            assert len(op) == 1
            if _in_while_loop(self._control_flow_node_map, node_name_prefix):
                if node_name_prefix not in self._loop_var_order:
                    self._loop_var_order[node_name_prefix] = []
                if node.name.endswith("Switch"):
                    self._loop_var_order[node_name_prefix].append(0)
                else:
                    self._loop_var_order[node_name_prefix].\
                        append(int(node.name.split("Switch_")[-1]))
                self._loops[node_name_prefix].loop_vars.append(op[0])
            else:
                if node_name_prefix not in self._branches:
                    self._branches[node_name_prefix] = Branch()
                self._branches[node_name_prefix].cond = cond[0]
        elif node.op == "NextIteration":
            if node_name_prefix not in self._loop_body_order:
                self._loop_body_order[node_name_prefix] = []
            if node.name.endswith("NextIteration"):
                self._loop_body_order[node_name_prefix].append(0)
            else:
                self._loop_body_order[node_name_prefix].\
                    append(int(node.name.split("NextIteration_")[-1]))
            op = self._backtrack_construct(node.input[0])

            assert len(op) == 1
            self._loops[node_name_prefix].body.append(op[0])
        else:
            raise Exception("Cannot identify control flow operator: " +
                            "{}".format(node.op))

        return op

    def _convert_operator(self, op_name, inputs, attrs,
                          graph, identity_list=None, convert_map=None):
        """Convert from Tensorflow operator to relay operator.
        The converter must specify conversions explicitly for incompatible name, and
        apply handlers to operator attributes.

        Parameters
        ----------
        op_name : str
            Operator name, such as Conv2D, AvgPool
        inputs : list of relay.op
            List of input symbols.
        attrs : dict
            Dict of operator attributes
        identity_list : list
            List of operators that don't require conversion
        convert_map : dict
            Dict of name : callable, where name is the op's name that
            require conversion to relay, callable are functions which
            take attrs and return (new_op_name, new_attrs)

        Returns
        -------
        sym : relay.op
            Converted relay operator
        """
        identity_list = identity_list if identity_list else _identity_list
        convert_map = convert_map if convert_map else _convert_map
        convert_map_rnn = _convert_map_rnn
        if op_name in identity_list:
            sym = get_relay_op(op_name)(*inputs, **attrs)
        elif op_name in convert_map:
            if _need_prelude_for_shape_inference(op_name):
                sym = convert_map[op_name](inputs, attrs, self._params, self._prelude)
            else:
                sym = convert_map[op_name](inputs, attrs, self._params, self._mod)

        elif op_name in convert_map_rnn:
            sym = self._convert_rnn_operator(op_name, inputs, attrs,
                                             self._params, graph,
                                             convert_map_rnn)
        else:
            raise NotImplementedError("Operator {} not implemented.".format(op_name))
        return sym

    def _backtrack_construct(self, node_name):
        """Convert a specific tensorflow node to relay expression.

        If any of its ancestor node is not converted yet, backtrack as
        far as input node and covert all nodes on the path.

        This is required when parsing control flow nodes, since the parsing
        order may not follow the original graph def.

        Parameters
        ----------
        node_name : str
            Tensorflow node name.

        Returns
        -------
        op : relay.Expr
            Converted relay expression
        """
        node_name = node_name.split(':')[0].split("^")[-1]

        if node_name not in self._nodes:
            node = self._tf_node_map[node_name]
            attr = self._parse_attr(node.attr)

            if node.op in _control_flow_nodes:
                attr = self._parse_attr(node.attr)
                op = self._convert_control_flow_operator(node, [],
                                                         attr,
                                                         self._control_flow_node_map)
            else:
                attr["_output_shapes"] = self._output_shapes[node_name]
                attr["_node_name"] = node.name
                attr["_target_layout"] = self._layout
                inputs = []
                for iname in node.input:
                    in_op = self._backtrack_construct(iname)
                    if isinstance(in_op, _expr.TupleWrapper):
                        tn = iname.split(':')
                        tensor_slot = int(tn[1]) if len(tn) > 1 else 0
                        in_op = in_op[tensor_slot]
                    else:
                        in_op = in_op[0]

                    inputs.append(in_op)
                op = self._convert_operator(node.op, inputs, attr, self._graph)

            if isinstance(op, np.ndarray):
                self._params[node.name] = tvm.nd.array(op)
                op = [_expr.var(node.name,
                                shape=self._params[node.name].shape,
                                dtype=self._params[node.name].dtype)]

            elif isinstance(op, (_expr.Expr, _expr.TupleGetItem)):
                op = [op]

            node_hash = s_hash(op) if isinstance(op, _expr.Tuple) else s_hash(op[0])
            self._hash2tfnode[node_hash] = node
            self._nodes[node_name] = op

        return self._nodes[node_name]

def from_tensorflow(graph, layout="NHWC", shape=None, outputs=None):
    """Load tensorflow graph which is a python tensorflow graph object into relay.
    The companion parameters will be handled automatically.

    Parameters
    ----------
    graph : GraphDef object
        Tensorflow GraphDef

    layout : target layout to be used (Optional)
        NCHW only supported now to enable NHWC models on GPU.

    shape : Dictionary of input dimensions (Optional)
        Graph level input shape dictionary.

    outputs : List of output tensor names (Optional)
        if not specified then the last node is assumed as graph output.

    Returns
    -------
    mod : tvm.IRModule
        The module that optimizations will be performed on.

    params : dict of str to tvm.nd.NDArray
        Dict of converted parameters stored in tvm.nd.NDArray format
    """
    g = GraphProto()
    mod, params = g.from_tensorflow(graph, layout, shape, outputs)
    return mod, params
