parameters: (sparse_indices,
                      output_shape,
                      sparse_values,
                      default_value=0,
                      validate_indices=True,
                      name=None)
return_statement: return gen_sparse_ops.sparse_to_dense(
        sparse_indices,
        output_shape,
        sparse_values,
        default_value=default_value,
        validate_indices=validate_indices,
        name=name)
docstring: Converts a sparse representation into a dense tensor.
  
    Builds an array `dense` with shape `output_shape` such that
  
    ```python
    # If sparse_indices is scalar
    dense[i] = (i == sparse_indices ? sparse_values : default_value)
  
    # If sparse_indices is a vector, then for each i
    dense[sparse_indices[i]] = sparse_values[i]
  
    # If sparse_indices is an n by d matrix, then for each i in [0, n)
    dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
    ```
  
    All other values in `dense` are set to `default_value`.  If `sparse_values`
    is a scalar, all sparse indices are set to this single value.
  
    Indices should be sorted in lexicographic order, and indices must not
    contain any repeats. If `validate_indices` is True, these properties
    are checked during execution.
  
    Args:
      sparse_indices: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
        `sparse_indices[i]` contains the complete index where `sparse_values[i]`
        will be placed.
      output_shape: A 1-D `Tensor` of the same type as `sparse_indices`.  Shape
        of the dense output tensor.
      sparse_values: A 0-D or 1-D `Tensor`.  Values corresponding to each row of
        `sparse_indices`, or a scalar value to be used for all sparse indices.
      default_value: A 0-D `Tensor` of the same type as `sparse_values`.  Value
        to set for indices not specified in `sparse_indices`.  Defaults to zero.
      validate_indices: A boolean value.  If True, indices are checked to make
        sure they are sorted in lexicographic order and that there are no repeats.
      name: A name for the operation (optional).
  
    Returns:
      Dense `Tensor` of shape `output_shape`.  Has the same type as
      `sparse_values`.
docstring_summary: Converts a sparse representation into a dense tensor.
docstring_tokens: Converts a sparse representation into a dense tensor .
function: def sparse_to_dense(sparse_indices,
                      output_shape,
                      sparse_values,
                      default_value=0,
                      validate_indices=True,
                      name=None):
    """Converts a sparse representation into a dense tensor.
  
    Builds an array `dense` with shape `output_shape` such that
  
    ```python
    # If sparse_indices is scalar
    dense[i] = (i == sparse_indices ? sparse_values : default_value)
  
    # If sparse_indices is a vector, then for each i
    dense[sparse_indices[i]] = sparse_values[i]
  
    # If sparse_indices is an n by d matrix, then for each i in [0, n)
    dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
    ```
  
    All other values in `dense` are set to `default_value`.  If `sparse_values`
    is a scalar, all sparse indices are set to this single value.
  
    Indices should be sorted in lexicographic order, and indices must not
    contain any repeats. If `validate_indices` is True, these properties
    are checked during execution.
  
    Args:
      sparse_indices: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
        `sparse_indices[i]` contains the complete index where `sparse_values[i]`
        will be placed.
      output_shape: A 1-D `Tensor` of the same type as `sparse_indices`.  Shape
        of the dense output tensor.
      sparse_values: A 0-D or 1-D `Tensor`.  Values corresponding to each row of
        `sparse_indices`, or a scalar value to be used for all sparse indices.
      default_value: A 0-D `Tensor` of the same type as `sparse_values`.  Value
        to set for indices not specified in `sparse_indices`.  Defaults to zero.
      validate_indices: A boolean value.  If True, indices are checked to make
        sure they are sorted in lexicographic order and that there are no repeats.
      name: A name for the operation (optional).
  
    Returns:
      Dense `Tensor` of shape `output_shape`.  Has the same type as
      `sparse_values`.
    """
    return gen_sparse_ops.sparse_to_dense(
        sparse_indices,
        output_shape,
        sparse_values,
        default_value=default_value,
        validate_indices=validate_indices,
        name=name)
function_tokens: def sparse_to_dense ( sparse_indices , output_shape , sparse_values , default_value = 0 , validate_indices = True , name = None ) : return gen_sparse_ops . sparse_to_dense ( sparse_indices , output_shape , sparse_values , default_value = default_value , validate_indices = validate_indices , name = name )