023331ec2a7b0086abfc81eca16c84a1692ee653,keras/layers/convolutional.py,ZeroPadding3D,__init__,#ZeroPadding3D#Any#Any#,1625

Before Change

        super(ZeroPadding3D, self).__init__(**kwargs)
        if data_format == "default":
            data_format = K.image_data_format()
        self.padding = tuple(padding)
        if data_format not in {"channels_last", "channels_first"}:
            raise ValueError("data_format must be in {"channels_last", "channels_first"}.")
        self.data_format = data_format
        self.input_spec = [InputSpec(ndim=5)]

After Change

    def __init__(self, padding=(1, 1, 1), data_format=None, **kwargs):
        super(ZeroPadding3D, self).__init__(**kwargs)
        self.data_format = conv_utils.normalize_data_format(data_format)
        if isinstance(padding, int):
            self.padding = ((padding, padding), (padding, padding), (padding, padding))
        if hasattr(padding, "__len__"):
            if len(padding) != 3:
                raise ValueError("TODO")
            dim1_padding = conv_utils.normalize_tuple(padding[0], 2,
                                                      "1st entry of padding")
            dim2_padding = conv_utils.normalize_tuple(padding[1], 2,
                                                      "2nd entry of padding")
            dim3_padding = conv_utils.normalize_tuple(padding[2], 2,
                                                      "3rd entry of padding")
            self.padding = (dim1_padding, dim2_padding, dim3_padding)
        else:
            raise ValueError("`padding` should be either an int, "
                             "a tuple of 3 ints "
                             "(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad), "
                             "or a tuple of 3 tuples of 2 ints "
                             "((left_dim1_pad, right_dim1_pad),"
                             " (left_dim2_pad, right_dim2_pad),"
                             " (left_dim3_pad, right_dim2_pad)). "
                             "Found: " + str(padding))
        self.input_spec = [InputSpec(ndim=5)]

    def get_output_shape_for(self, input_shape):
        if self.data_format == "channels_first":

Instances