0c4f11cf0dcbd6c01641705d9040907bdb686fca,tensorlayer/layers/convolution/simplified_deconv.py,DeConv2d,build,#DeConv2d#Any#,120

Before Change

            // dtype=tf.float32,
            name=self.name,
        )
        if self.data_format == "channels_first":
            self.in_channels = inputs_shape[1]
        else:
            self.in_channels = inputs_shape[-1]
        _out = self.layer(
            tf.convert_to_tensor(np.random.uniform(size=inputs_shape), dtype=np.float32)
        )  //np.random.uniform([1] + list(inputs_shape)))  // initialize weights
        outputs_shape = _out.shape

After Change

            // dtype=tf.float32,
            name=self.name,
        )
        if inputs_shape is not None:
            self.in_channels = inputs_shape[1 if self.data_format == "channels_first" else -1]
        elif self.in_channels is not None:
            inputs_shape = [1, self.in_channels, 1, 1] if self.data_format == "channels_first" else [1, 1, 1, self.in_channels]
        else:
            raise ValueError("Either inputs_shape or in_channels must be specified for build.")
        _out = self.layer(
            tf.convert_to_tensor(np.random.uniform(size=inputs_shape), dtype=np.float32)
        )  //np.random.uniform([1] + list(inputs_shape)))  // initialize weights
        outputs_shape = _out.shape

Instances