docstr: In the homoskedastic case, the parameters are only used to infer the required shape.
  Here are the possible scenarios:
  - non-batched noise, non-batched input, non-MT -> noise_diag shape is `n`
  - non-batched noise, non-batched input, MT -> noise_diag shape is `nt`
  - non-batched noise, batched input, non-MT -> noise_diag shape is `b x n` with b' the broadcasted batch shape
  - non-batched noise, batched input, MT -> noise_diag shape is `b x nt`
  - batched noise, non-batched input, non-MT -> noise_diag shape is `b x n`
  - batched noise, non-batched input, MT -> noise_diag shape is `b x nt`
  - batched noise, batched input, non-MT -> noise_diag shape is `b' x n`
  - batched noise, batched input, MT -> noise_diag shape is `b' x nt`
  where `n` is the number of evaluation points and `t` is the number of tasks (i.e. `num_tasks` of self.noise).
  So bascially the shape is always `b' x nt`, with `b'` appropriately broadcast from the noise parameter and
  input batch shapes. `n` and the input batch shape are determined either from the shape arg or from the params
  input. For this it is sufficient to take in a single `shape` arg, with the convention that shape[:-1] is the
  batch shape of the input, and shape[-1] is `n`.
  
  If a "noise" kwarg (a Tensor) is provided, this noise is used directly.
code: def forward(
      self,
      *params: Any,
      shape: Optional[torch.Size] = None,
      **kwargs: Any,
  ) -> DiagLinearOperator:
      """In the homoskedastic case, the parameters are only used to infer the required shape.
      Here are the possible scenarios:
      - non-batched noise, non-batched input, non-MT -> noise_diag shape is `n`
      - non-batched noise, non-batched input, MT -> noise_diag shape is `nt`
      - non-batched noise, batched input, non-MT -> noise_diag shape is `b x n` with b' the broadcasted batch shape
      - non-batched noise, batched input, MT -> noise_diag shape is `b x nt`
      - batched noise, non-batched input, non-MT -> noise_diag shape is `b x n`
      - batched noise, non-batched input, MT -> noise_diag shape is `b x nt`
      - batched noise, batched input, non-MT -> noise_diag shape is `b' x n`
      - batched noise, batched input, MT -> noise_diag shape is `b' x nt`
      where `n` is the number of evaluation points and `t` is the number of tasks (i.e. `num_tasks` of self.noise).
      So bascially the shape is always `b' x nt`, with `b'` appropriately broadcast from the noise parameter and
      input batch shapes. `n` and the input batch shape are determined either from the shape arg or from the params
      input. For this it is sufficient to take in a single `shape` arg, with the convention that shape[:-1] is the
      batch shape of the input, and shape[-1] is `n`.
  
      If a "noise" kwarg (a Tensor) is provided, this noise is used directly.
      """
      if "noise" in kwargs:
          return DiagLinearOperator(kwargs.get("noise"))
      if shape is None:
          p = params[0] if torch.is_tensor(params[0]) else params[0][0]
          shape = p.shape if len(p.shape) == 1 else p.shape[:-1]
      noise = self.noise
      *batch_shape, n = shape
      noise_batch_shape = (
          noise.shape[:-1] if noise.dim() > 1 else torch.Size()
      )
      num_tasks = noise.shape[-1]
      batch_shape = torch.broadcast_shapes(
          noise_batch_shape, batch_shape
      )
      noise = noise.unsqueeze(-2)
      noise_diag = noise.expand(*batch_shape, 1, num_tasks).contiguous()
      if num_tasks == 1:
          noise_diag = noise_diag.view(*batch_shape, 1)
      if noise_diag.shape[-1] != 1:
          noise_diag = noise_diag.unsqueeze(-1)
      return ConstantDiagLinearOperator(noise_diag, diag_shape=n)
  
imports: import warnings
  from typing import Any, Optional, Union
  import torch
  from linear_operator.operators import ConstantDiagLinearOperator, DiagLinearOperator, LinearOperator, ZeroLinearOperator
  from torch import Tensor
  from torch.nn import Parameter
  from ..None import settings
  from ..constraints import GreaterThan
  from ..distributions import MultivariateNormal
  from ..module import Module
  from ..utils.warnings import NumericalWarning
label_desc: Use image node_id 7 for calling the _HomoskedasticNoiseBase obj's underlying member method code with example usage: obj.forward() and returns: ConstantDiagLinearOperator, DiagLinearOperator