code: def _convert_time_distributed(
      inexpr, keras_layer, etab, data_layout, input_shape=None
  ):
      # TimeDistributed: split input tensor along the second dimension (assumed to be time),
      # apply inner layer to each split individually,
      # and then combine the results
      if input_shape is None:
          input_shape = keras_layer.input_shape
  
      assert (
          len(input_shape) >= 2
      ), "Input to TimeDistributed must have at least two dimensions"
  
      inner_layer = keras_layer.layer
      inner_input_shape = [
          d for (i, d) in enumerate(input_shape) if i != 1
      ]
  
      # for NDHWC, inner data layout will drop the D
      inner_data_layout = data_layout
      if data_layout == "NDHWC":
          inner_data_layout = "NHWC"
  
      # some code duplication from keras_op_to_relay
      # but it's useful to avoid cluttering the etab
      inner_layer_op_name = type(keras_layer.layer).__name__
      if inner_layer_op_name not in _convert_map:
          raise tvm.error.OpNotImplemented(
              f"The inner layer for TimeDistributed {inner_layer_op_name} is not supported for"
              f" frontend Keras."
          )
  
      conversion_func = lambda expr: _convert_map[inner_layer_op_name](
          expr,
          inner_layer,
          etab,
          inner_data_layout,
          input_shape=inner_input_shape,
      )
  
      split_dim = input_shape[1]
      split_input = _op.split(inexpr, split_dim, 1)
  
      split_shape = list(input_shape)
      if split_shape[0] is None:
          split_shape[0] = 1
      split_shape[1] = 1
  
      split_var = new_var(
          "time_distributed_split",
          type_annotation=TupleType(
              [
                  TensorType(split_shape, dtype="float32")
                  for i in range(split_dim)
              ]
          ),
      )
  
      # For each split, squeeze away the second dimension,
      # apply the inner layer.
      # Afterwards, combine the transformed splits back along
      # the second dimension using stack
      splits = [
          conversion_func(
              _op.squeeze(_expr.TupleGetItem(split_var, i), axis=[1])
          )
          for i in range(split_dim)
      ]
  
      return _expr.Let(
          split_var, split_input.astuple(), _op.stack(splits, axis=1)
      )
  
imports: import dis
  import sys
  import numpy
  import tvm
  from tvm.ir import IRModule, TensorType, TupleType
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from ..None import op
  from ...None import nd
  from .common import ExprTable, new_var
label_desc: Use image node_id 34 for calling a global function with example usage: _convert_time_distributed(inexpr, keras_layer, etab, data_layout, input_shape) and returns: _expr