class_docstr: Core Embedding Column.
class_docstr_tok: ["Core","Embedding","Column","."]
code: def __init__(
      self,
      categorical_column,
      dimension,
      combiner="mean",
      layer_creator=None,
      ckpt_to_load_from=None,
      tensor_name_in_ckpt=None,
      max_norm=None,
      trainable=True,
      max_sequence_length=0,
      learning_rate_fn=None,
      use_safe_embedding_lookup=True,
      bypass_scope_validation=False,
  ):
      _TPUBaseEmbeddingColumn.__init__(
          self,
          categorical_column,
          max_sequence_length=max_sequence_length,
          learning_rate_fn=learning_rate_fn,
      )
      self._key = None
      # If true, scope validation is skipped to allow the same column to be used
      # in multiple variable scopes. By default, this is False, and we expect a
      # 1:1 mapping between feature columns and scopes.
      self._bypass_scope_validation = bypass_scope_validation
  
imports: import math
  from tensorflow.python.feature_column import feature_column
  from tensorflow.python.feature_column import feature_column_lib
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.tpu import tpu
  from tensorflow.python.tpu import tpu_function
  from tensorflow.python.tpu import tpu_replication
label_desc: Use image node_id 2 to create a new _TPUEmbeddingColumn object from inherited base classes: _TPUBaseEmbeddingColumn, fc with example: obj = _TPUEmbeddingColumn(categorical_column, dimension, combiner, layer_creator, ckpt_to_load_from, tensor_name_in_ckpt, max_norm, trainable, max_sequence_length, learning_rate_fn, use_safe_embedding_lookup, bypass_scope_validation)