class_docstr: Point Cloud Transformer Classifier.
code: def __call__(self, inputs, train: bool = False, debug: bool = False):
      if self.attention_type == "standard":
          output = PointCloudTransformerEncoder(
              in_dim=self.in_dim,
              feature_dim=self.feature_dim,
              kernel_size=self.kernel_size,
              attention_fn_configs=self.attention_fn_configs,
              use_attention_masking=self.use_attention_masking,
              use_knn_mask=self.use_knn_mask,
              nearest_neighbour_count=self.nearest_neighbour_count,
              mask_function=self.mask_function,
          )(inputs, train=train, debug=debug)
  
      # Max Pooling
      output = jnp.max(output, axis=1, keepdims=False)
      # LBR Block 1
      output = nn.Dense(4 * self.feature_dim, use_bias=True)(output)
      output = nn.BatchNorm(use_running_average=not train)(output)
      output = nn.leaky_relu(output, negative_slope=0.2)
      output = nn.Dropout(
          rate=self.dropout_rate, deterministic=not train
      )(output)
      # LBR Block 2
      output = nn.Dense(2 * self.feature_dim, use_bias=True)(output)
      output = nn.BatchNorm(use_running_average=not train)(output)
      output = nn.leaky_relu(output, negative_slope=0.2)
      output = nn.Dropout(
          rate=self.dropout_rate, deterministic=not train
      )(output)
      # Classification head
      output = nn.Dense(self.num_classes, use_bias=True)(output)
      return output
  
imports: from typing import Any
  import flax.linen
  from flax.linen.initializers import zeros
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models.multilabel_classification_model import MultiLabelClassificationModel
  from scenic.projects.performer import performer
label_desc: Use image node_id 1 for calling the PointCloudTransformerClassifier obj's underlying member method code with example usage: obj.__call__(inputs, train, debug) and returns: output