return ApiBuilder::Instance().GetBuilder()->Build<WhileOp>(cond,

loop_values);

});

}

void GetUsedExternalValueImpl(

ss << "define_op_name=" << op_result.owner()->name();

ss << ", index=" << op_result.index();

} else if (auto arg = v.dyn_cast<BlockArgument>()) {

}

ss << ", dtype=" << v.type();

if (v.type().isa<paddle::dialect::AllocatedDenseTensorType>()) {

}

return op_list;

})

.def("__exit__",

[](Block &self, py::object, py::object, py::object) {

ApiBuilder::Instance().PopInsertionPoint();

from .framework import monkey_patch_variable

from .framework import monkey_patch_math_tensor

monkey_patch_variable()

monkey_patch_math_tensor()

monkey_patch_program()

from .framework import (

)

from . import core # noqa: F401

from .program_patch import monkey_patch_program # noqa: F401

__all__ = []

from paddle.base.libpaddle import DataType

from paddle.base.wrapped_decorator import wrap_decorator

_supported_int_dtype_ = [

DataType.BOOL,

def create_tensor_with_batchsize(ref_var, value, dtype):

value = float(value)

batch_dim = -1

out_shape = []

return out

def safe_get_dtype(var):

try:

dtype = var.dtype

def place(self):

"""

warnings.warn(

)

@property

def _ndim(self):

"""

return len(self.shape)

def ndimension(self):

"""

return len(self.shape)

def dim(self):

"""

if len(self.shape) > 1:

raise TypeError(

)

return self

def astype(self, dtype):

"""

# do nothing

pass

lhs_dtype = safe_get_dtype(self)

if reverse:

for elem in self.shape:

if elem < 0:

self, other_var, lhs_dtype

)

break

else:

# when break is not triggered, enter the else branch

)

else:

# add fill_op to current_block

[],

lhs_dtype,

other_var,

)

# 3. unify right var type to left var

if lhs_dtype != rhs_dtype:

if reverse:

tmp = self

if (

python_api == paddle.divide

) and self.dtype in _supported_int_dtype_:

self = paddle.cast(self, DataType.FLOAT32)

return out

__impl__.__doc__ = """

__impl__.__name__ = method_name

return __impl__

@property

def _size_(self):

"""

def clone(self):

"""

def append(self, var):

"""

if not self.is_dense_tensor_array_type():

raise TypeError(

self.type()

)

)

import paddle

('cpu', cpu),

('cuda', cuda),

('place', place),

),

]

method_name = method[0]

method_impl = method[1]

# Handling Tensor Methods

import paddle.tensor

for method_name in paddle.tensor.tensor_method_func:

continue

method_impl = getattr(paddle.tensor, method_name, None)

if method_impl:

# Bit operation symbol

for magic_method, origin_method in paddle.tensor.magic_method_func:

impl = getattr(paddle.tensor, origin_method, None)

if impl:

# Handling __getitem__

from ..base.variable_index import _getitem_static