class_docstr: Type specification for a `tf.IndexedSlices`.
class_docstr_tok: ["Type","specification","for","a","`","tf.IndexedSlices","`","."]
docstr: Constructs a type specification for a `tf.IndexedSlices`.
  
  Args:
    shape: The dense shape of the `IndexedSlices`, or `None` to allow any
      dense shape.
    dtype: `tf.DType` of values in the `IndexedSlices`.
    indices_dtype: `tf.DType` of the `indices` in the `IndexedSlices`.  One
      of `tf.int32` or `tf.int64`.
    dense_shape_dtype: `tf.DType` of the `dense_shape` in the `IndexedSlices`.
      One of `tf.int32`, `tf.int64`, or `None` (if the `IndexedSlices` has
      no `dense_shape` tensor).
    indices_shape: The shape of the `indices` component, which indicates
      how many slices are in the `IndexedSlices`.
code: def __init__(
      self,
      shape=None,
      dtype=dtypes.float32,
      indices_dtype=dtypes.int64,
      dense_shape_dtype=None,
      indices_shape=None,
  ):
      """Constructs a type specification for a `tf.IndexedSlices`.
  
      Args:
        shape: The dense shape of the `IndexedSlices`, or `None` to allow any
          dense shape.
        dtype: `tf.DType` of values in the `IndexedSlices`.
        indices_dtype: `tf.DType` of the `indices` in the `IndexedSlices`.  One
          of `tf.int32` or `tf.int64`.
        dense_shape_dtype: `tf.DType` of the `dense_shape` in the `IndexedSlices`.
          One of `tf.int32`, `tf.int64`, or `None` (if the `IndexedSlices` has
          no `dense_shape` tensor).
        indices_shape: The shape of the `indices` component, which indicates
          how many slices are in the `IndexedSlices`.
      """
      self._shape = tensor_shape.as_shape(shape)
      self._values_dtype = dtypes.as_dtype(dtype)
      self._indices_dtype = dtypes.as_dtype(indices_dtype)
      if dense_shape_dtype is None:
          self._dense_shape_dtype = None
      else:
          self._dense_shape_dtype = dtypes.as_dtype(dense_shape_dtype)
      self._indices_shape = tensor_shape.as_shape(
          indices_shape
      ).with_rank(1)
  
imports: import collections
  import warnings
  import numpy
  from tensorflow.core.protobuf import struct_pb2
  from tensorflow.python import tf2
  from tensorflow.python.eager import context
  from tensorflow.python.framework import composite_tensor
  from tensorflow.python.framework import composite_tensor_gradient
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_conversion_registry
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.framework import type_spec
  from tensorflow.python.ops import gen_math_ops
  from tensorflow.python.saved_model import nested_structure_coder
  from tensorflow.python.types import internal
  from tensorflow.python.util.compat import collections_abc
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new IndexedSlicesSpec object from inherited base classes: type_spec with example: obj = IndexedSlicesSpec(shape, dtype, indices_dtype, dense_shape_dtype, indices_shape)