docstr: This method initializes the trainer, model and sampler for the agent.
  Defaultly the policy_network is LinearRegressionUCB, trainer is joint model LinUCBTrainer, and sampler is GreedyActionSampler.
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code: def make_agent(cls, feature_dim: int):
      """
      This method initializes the trainer, model and sampler for the agent.
      Defaultly the policy_network is LinearRegressionUCB, trainer is joint model LinUCBTrainer, and sampler is GreedyActionSampler.
      """
      params = LinUCBTrainerParameters()
      policy_network = LinearRegressionUCB(input_dim=feature_dim)
      policy = Policy(
          scorer=policy_network, sampler=GreedyActionSampler()
      )
      # pyre-ignore
      trainer = LinUCBTrainer(policy, **params.asdict())
      agent = cls(
          trainer=trainer,
          feature_dim=feature_dim,
          policy=policy,
      )
      return agent
  
imports: import random
  from dataclasses import replace
  from typing import Any, Optional, Tuple
  import reagent.core.types
  import torch
  from reagent.core.types import CBInput
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.samplers.discrete_sampler import GreedyActionSampler
  from reagent.gym.types import Sampler, Scorer
  from reagent.models.linear_regression import LinearRegressionUCB
  from reagent.training.cb.base_trainer import BaseCBTrainerWithEval
  from reagent.training.cb.linucb_trainer import LinUCBTrainer
  from reagent.training.parameters import LinUCBTrainerParameters
label_desc: Use image node_id 3 for calling the DynamicBanditAgent obj's underlying member method code with example usage: obj.make_agent(cls, feature_dim) and returns: agent