code: def maximum_likelihood_estimate_sgd(
      distr_output: DistributionOutput,
      samples: torch.Tensor,
      init_biases: List[np.ndarray] = None,
      num_epochs: PositiveInt = PositiveInt(5),
      learning_rate: PositiveFloat = PositiveFloat(1e-2),
  ):
      arg_proj = distr_output.get_args_proj(in_features=1)
      if init_biases is not None:
          for param, bias in zip(arg_proj.proj, init_biases):
              nn.init.constant_(param.bias, bias)
      dummy_data = torch.ones((len(samples), 1))
      dataset = TensorDataset(dummy_data, samples)
      train_data = DataLoader(
          dataset, batch_size=BATCH_SIZE, shuffle=True
      )
      optimizer = SGD(arg_proj.parameters(), lr=learning_rate)
      for e in range(num_epochs):
          cumulative_loss = 0
          num_batches = 0
          for i, (data, sample_label) in enumerate(train_data):
              optimizer.zero_grad()
              distr_args = arg_proj(data)
              distr = distr_output.distribution(distr_args)
              loss = -distr.log_prob(sample_label).mean()
              loss.backward()
              clip_grad_norm_(arg_proj.parameters(), 10.0)
              optimizer.step()
              num_batches += 1
              cumulative_loss += loss.item()
      if len(distr_args[0].shape) == 1:
          return [
              param.detach().numpy()
              for param in arg_proj(torch.ones((1, 1)))
          ]
      return [
          param[0].detach().numpy()
          for param in arg_proj(torch.ones((1, 1)))
      ]
  
imports: from typing import List
  import numpy
  import pytest
  import torch
  import torch.nn
  from scipy.special import softmax
  from torch.distributions import Beta, Gamma, NegativeBinomial, Normal, Poisson, StudentT
  from torch.nn.utils import clip_grad_norm_
  from torch.optim import SGD
  from torch.utils.data import DataLoader, TensorDataset
  from gluonts.pydantic import PositiveFloat, PositiveInt
  from gluonts.torch.distributions import BetaOutput, DistributionOutput, GammaOutput, NegativeBinomialOutput, NormalOutput, PoissonOutput, SplicedBinnedPareto, SplicedBinnedParetoOutput, StudentTOutput
label_desc: Use image node_id 3 for calling a global function with example usage: maximum_likelihood_estimate_sgd(distr_output, samples, init_biases, num_epochs, learning_rate) and returns: unknown, unknown