Coding Custom Likelihood Pymc3

I am struggling to implement a linear regression in pymc3 with a custom likelihood.

I previously posted this question on CrossValidated & it was recommended to post here as the question is more code orientated (closed post here)

Suppose you have two independent variables x1, x2 and a target variable y, as well as an indicator variable called delta.

• When delta is 0, the likelihood function is standard least squares
• When delta is 1, the likelihood function is the least squares contribution only when the target variable is greater than the prediction

Example snippet of observed data:

x_1  x_2  𝛿   observed_target  
10    1   0   100              
20    2   0   50               
5    -1   1   200             
10   -2   1   100             

Does anyone know how this can be implemented in pymc3? As a starting point...

model =  pm.Model()
with model as ttf_model:

  intercept = pm.Normal('param_intercept', mu=0, sd=5)
  beta_0 = pm.Normal('param_x1', mu=0, sd=5)
  beta_1 = pm.Normal('param_x2', mu=0, sd=5)
  std = pm.HalfNormal('param_std', beta = 0.5)

  x_1 = pm.Data('var_x1', df['x1'])
  x_2 = pm.Data('var_x2', df['x2'])

  mu = (intercept + beta_0*x_0 + beta_1*x_1)

from Coding Custom Likelihood Pymc3