def __call__(self, *input_tensor_list):
tensor_row = [
cde.Tensor(np.asarray(tensor)) for tensor in input_tensor_list
]
callable_op = cde.Execute(self.parse())
output_tensor_list = callable_op(tensor_row)
for i, element in enumerate(output_tensor_list):
arr = element.as_array()
if arr.dtype.char == 'S':
output_tensor_list[i] = np.char.decode(arr)
else:
output_tensor_list[i] = arr
return output_tensor_list[0] if len(
output_tensor_list) == 1 else tuple(output_tensor_list)
def __call__(self, *input_tensor_list):
tensor_row = []
for tensor in input_tensor_list:
try:
tensor_row.append(cde.Tensor(np.asarray(tensor)))
except RuntimeError:
raise TypeError("Invalid user input. Got {}: {}, cannot be converted into tensor." \
.format(type(tensor), tensor))
callable_op = cde.Execute(self.parse())
output_tensor_list = callable_op(tensor_row)
for i, element in enumerate(output_tensor_list):
arr = element.as_array()
if arr.dtype.char == 'S':
output_tensor_list[i] = np.char.decode(arr)
else:
output_tensor_list[i] = arr
return output_tensor_list[0] if len(output_tensor_list) == 1 else tuple(output_tensor_list)
def __call__(self, input_tensor):
if not isinstance(input_tensor, list):
input_list = [input_tensor]
else:
input_list = input_tensor
tensor_list = []
for tensor in input_list:
if not isinstance(tensor, str):
raise TypeError("Input should be string or list of strings, got {}.".format(type(tensor)))
tensor_list.append(cde.Tensor(np.asarray(tensor)))
callable_op = cde.Execute(self.parse())
output_list = callable_op(tensor_list)
for i, element in enumerate(output_list):
arr = element.as_array()
if arr.dtype.char == 'S':
output_list[i] = to_str(arr)
else:
output_list[i] = arr
if not isinstance(input_tensor, list) and len(output_list) == 1:
output_list = output_list[0]
return output_list
