jectoryTransformerModel
        >>> import torch

        >>> model = TrajectoryTransformerModel.from_pretrained(
        ...     "CarlCochet/trajectory-transformer-halfcheetah-medium-v2"
        ... )
        >>> model.to(device)
        >>> model.eval()

        >>> observations_dim, action_dim, batch_size = 17, 6, 256
        >>> seq_length = observations_dim + action_dim + 1

        >>> trajectories = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(batch_size)]).to(
        ...     device
        ... )
        >>> targets = torch.LongTensor([np.random.permutation(self.seq_length) for _ in range(batch_size)]).to(device)

        >>> outputs = model(
        ...     trajectories,
        ...     targets=targets,
        ...     use_cache=True,
        ...     output_attentions=True,
        ...     output_hidden_states=True,
        ...     return_dict=True,
        ... )
        ```
        Nz.Cannot forward, model block size is exhausted.zZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Fr