How to know use learning rate finder?

[talhaanwarch]

class OurModel(LightningModule):
  def __init__(self,learning_rate):
    super(OurModel,self).__init__()
    self.learning_rate = learning_rate
    self.lr=1e-3
    self.layer=model()
  def forward(self,x):
    return self.layer(x)
  def configure_optimizers(self):
    opt=torch.optim.AdamW(self.parameters(), lr=(self.lr or self.learning_rate),weight_decay=1e-5)
    scheduler = ReduceLROnPlateau(opt,factor=0.5,patience=10,mode="min",verbose=True)
    return {'optimizer': opt,'lr_scheduler':scheduler,'monitor':'val_loss'}

model = OurModel()
lr_monitor = LearningRateMonitor(logging_interval='epoch')
logger = TensorBoardLogger("unet", name="logs")
checkpoint_callback = ModelCheckpoint(monitor='val_loss',dirpath='unet',
filename='{epoch}-{val_loss:.2f}-{val_dice:.2f}')

trainer = Trainer(max_epochs=100, auto_lr_find=True,
                  #tpu_cores=8,precision=16,
                  gpus=-1,precision=16,
                  logger=logger,
                  accumulate_grad_batches=8,
                  stochastic_weight_avg=True,
                  progress_bar_refresh_rate=120,
                  #auto_scale_batch_size='binsearch',
                  #resume_from_checkpoint='',
                  callbacks=[checkpoint_callback,lr_monitor]
                  )

I throw this error
TypeError: __init__() missing 1 required positional argument: 'learning_rate'