class_docstr: MNIST model based on CNN. xid/62622458.
class_docstr_tok: ["MNIST","model","based","on","CNN",".","xid\/62622458","."]
code: def datasets(self) -> list[pax_fiddle.Config[base_input.BaseInput]]:
      return [
          pax_fiddle.Config(
              MnistDaliInput,
              batch_size=self.BATCH_SIZE,
              is_training=True,
          ),
          # Eval dataset is not infinite.
          pax_fiddle.Config(
              MnistDaliInput,
              batch_size=self.BATCH_SIZE,
              is_training=False,
              reset_for_eval=True,
          ),
      ]
  
imports: from typing import cast
  import jax
  import jax.numpy
  from paxml import base_experiment
  from paxml import base_task
  from paxml import experiment_registry
  from paxml import learners
  from paxml import tasks_lib
  from praxis import base_input
  from praxis import base_layer
  from praxis import base_model
  from praxis import optimizers
  from praxis import pax_fiddle
  from praxis import py_utils
  from praxis import pytypes
  from praxis import schedules
  from praxis.layers import activations
  from praxis.layers import convolutions
  from praxis.layers import linears
  from praxis.layers import poolings
  from pax_examples.dali_pax_input import MnistDaliInput
label_desc: Use image node_id 2 for calling the MnistExperiment obj's underlying member method code with example usage: obj.datasets() and returns: list