message: Fixing LocallyConnected2D layer Save Model to tf issue
diff: @@ -458,6 +458,7 @@ class LocallyConnected2D(Layer):
  @tf_utils.shape_type_conversion
  def build(self, input_shape):
  + self.input_shape = (None, *input_shape)
  if self.data_format == 'channels_last':
  input_row, input_col = input_shape[1:-1]
  input_filter = input_shape[3]
  @@ -569,7 +570,18 @@ class LocallyConnected2D(Layer):
  elif self.data_format == 'channels_last':
  return (input_shape[0], rows, cols, self.filters)
  - def call(self, inputs):
  + def call(self, inputs=None):
  + if inputs is None:
  + if self.input_shape is not None:
  + input_shape = self.input_shape
  + else:
  + raise ValueError('No inputs passed ever to call()')
  + print("Warning: No inputs are passed to call().")
  + tmp_shape = [1 if x is None else x for x in input_shape]
  + inputs = array_ops.ones(tmp_shape)
  + else:
  + input_shape = inputs.shape
  + pre_computed_output_shape = self.compute_output_shape(input_shape)
  if self.implementation == 1:
  output = backend.local_conv(
  inputs, self.kernel, self.kernel_size, self.strides,
  @@ -578,12 +590,12 @@ class LocallyConnected2D(Layer):
  elif self.implementation == 2:
  output = local_conv_matmul(inputs, self.kernel, self.kernel_mask,
  - self.compute_output_shape(inputs.shape))
  + pre_computed_output_shape)
  elif self.implementation == 3:
  output = local_conv_sparse_matmul(inputs, self.kernel, self.kernel_idxs,
  self.kernel_shape,
  - self.compute_output_shape(inputs.shape))
  + pre_computed_output_shape)
  else:
  raise ValueError('Unrecognized implementation mode: %d.' %
  